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## steplibrary(lavaan)## This is lavaan 0.6-7## lavaan is BETA software! Please report any bugs.library(MplusAutomation)## Version: 0.8
## We work hard to write this free software. Please help us get credit by citing:
##
## Hallquist, M. N. & Wiley, J. F. (2018). MplusAutomation: An R Package for Facilitating Large-Scale Latent Variable Analyses in Mplus. Structural Equation Modeling, 25, 621-638. doi: 10.1080/10705511.2017.1402334.
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## %+%, alphalibrary(jmRtools)
library(asherR)
library(robumeta)## Warning: package 'robumeta' was built under R version 4.0.2Loading data
MTH_132_124_pre_survey <- read_sav("/Volumes/educ/CEPSE/Projects/SchmidtLab/Beymer_Projects/Beymer_Dissertation/Dissertation_Data/MTH_132_124/MTH_132_124_pre_survey_3_27_19.sav")
MTH_132_124_EOC <- read_sav("/Volumes/educ/CEPSE/Projects/SchmidtLab/Beymer_Projects/Beymer_Dissertation/Dissertation_Data/MTH_132_124//MTH_132_124_EOC_3_27_19.sav")
MTH_132_124_post_survey <- read_sav("/Volumes/educ/CEPSE/Projects/SchmidtLab/Beymer_Projects/Beymer_Dissertation/Dissertation_Data/MTH_132_124/MTH_132_124_post_Survey_3_27_19.sav")
MTH_132_124_demographics <- read_sav("/Volumes/educ/CEPSE/Projects/SchmidtLab/Beymer_Projects/Beymer_Dissertation/Dissertation_Data/MTH_132_124/MTH_132_124_demographics_grades_3_27_19.sav")
MTH_132_124_achievement <- read_sav("/Volumes/educ/CEPSE/Projects/SchmidtLab/Beymer_Projects/Beymer_Dissertation/Dissertation_Data/MTH_132_124/MTH_132_124_achievement_3_27_19.sav")
MTH_132_124_grades <- read_sav("/Volumes/educ/CEPSE/Projects/SchmidtLab/Beymer_Projects/Beymer_Dissertation/Dissertation_Data/MTH_132_124/MTH_132_124_course_grades_3_27_19.sav")
MTH_132_124_pre_survey[MTH_132_124_pre_survey$stud_id == "132_728", "participate_eoc"] <- 1Variable Creation
MTH_132_124_demographics$urm <- ifelse(MTH_132_124_demographics$black == 1 | MTH_132_124_demographics$hispanic == 1, 1 ,0)
MTH_132_124_pre_survey$hs_prep <- composite_mean_maker(MTH_132_124_pre_survey, pre_hs_prep_1, pre_hs_prep_2, pre_hs_prep_3, pre_hs_prep_4, pre_hs_prep_5)
MTH_132_124_demographics$credits_more_than_15 <- ifelse(MTH_132_124_demographics$Msu_Lt_Atmpt_Hours >= 15, 1, 0)
#Grand mean centering
MTH_132_124_pre_survey$pre_val_overall_z <- scale(MTH_132_124_pre_survey$pre_val_overall) %>% as.numeric()
MTH_132_124_pre_survey$pre_exp_overall_z <- scale(MTH_132_124_pre_survey$pre_exp_overall) %>% as.numeric()
MTH_132_124_pre_survey$hs_prep_z <- scale(MTH_132_124_pre_survey$hs_prep) %>% as.numeric()
MTH_132_124_pre_survey$pre_hours_work_z <- scale(MTH_132_124_pre_survey$pre_hours_work) %>% as.numeric()
MTH_132_124_pre_survey$pre_hours_math_prep_z <- scale(MTH_132_124_pre_survey$pre_hours_math_prep) %>% as.numeric()
MTH_132_124_pre_survey$pre_stem_int_z <- scale(MTH_132_124_pre_survey$pre_stem_int) %>% as.numeric()
MTH_132_124_demographics$Msu_Lt_Atmpt_Hours_z <- scale(MTH_132_124_demographics$Msu_Lt_Atmpt_Hours) %>% as.numeric()
MTH_132_124_achievement$Best_MPS_z <- scale(MTH_132_124_achievement$Best_MPS) %>% as.numeric()
MTH_132_124_pre_survey$pre_cost_te_overall_z <- scale(MTH_132_124_pre_survey$pre_cost_te_overall) %>% as.numeric()
MTH_132_124_pre_survey$pre_cost_oe_overall_z <- scale(MTH_132_124_pre_survey$pre_cost_oe_overall) %>% as.numeric()
MTH_132_124_pre_survey$pre_cost_lv_overall_z <- scale(MTH_132_124_pre_survey$pre_cost_lv_overall) %>% as.numeric()
MTH_132_124_pre_survey$pre_cost_em_overall_z <- scale(MTH_132_124_pre_survey$pre_cost_em_overall) %>% as.numeric()New data set for graph
Graphs <- c("stud_id", "week", "eoc_cost_te")
Graph<-MTH_132_124_EOC[Graphs]Joining Data Long
MTH_132_124_all <- left_join(MTH_132_124_EOC, MTH_132_124_pre_survey, by = "stud_id")
MTH_132_124_all <- left_join(MTH_132_124_all, MTH_132_124_post_survey, by = "stud_id")
MTH_132_124_all <- left_join (MTH_132_124_all, MTH_132_124_demographics, by = "stud_id")
MTH_132_124_all <- left_join (MTH_132_124_all, MTH_132_124_achievement, by = "stud_id")
MTH_132_124_all <- left_join (MTH_132_124_all, MTH_132_124_grades, by = "stud_id")Joining pre, post, demo, and achievement
MTH_132_124_pre_post <- left_join(MTH_132_124_pre_survey, MTH_132_124_post_survey, by = "stud_id")
MTH_132_124_pre_post <- left_join(MTH_132_124_pre_post, MTH_132_124_demographics, by = "stud_id")
MTH_132_124_pre_post <- left_join(MTH_132_124_pre_post, MTH_132_124_achievement, by = "stud_id")Converting to wide format
MTH_132_124_EOC_wide <- pivot_wider(MTH_132_124_EOC,
id_cols = c(stud_id),
names_from = week,
values_from = c(eoc_activity, eoc_comp, eoc_val, eoc_con,
eoc_int, eoc_future_goals, eoc_conc,
eoc_hard_work, eoc_enjoy, eoc_happy,
eoc_confused, eoc_bored, eoc_excited,
eoc_angry, eoc_anxious, eoc_frustrated,
eoc_cost_te, eoc_cost_oe, eoc_cost_lv,
eoc_cost_em, eoc_cost_psy, eoc_group,
eoc_coop, eoc_compete))Joining Data Long
# MTH_132_124_all_wide <- left_join(MTH_132_124_pre_survey, MTH_132_124_EOC_wide, by = "stud_id")
# MTH_132_124_all_wide <- left_join(MTH_132_124_all_wide, MTH_132_124_post_survey, by = "stud_id")
# MTH_132_124_all_wide <- left_join (MTH_132_124_all_wide, MTH_132_124_demographics, by = "stud_id")
# MTH_132_124_all_wide <- left_join (MTH_132_124_all_wide, MTH_132_124_achievement, by = "stud_id")
# MTH_132_124_all_wide <- left_join (MTH_132_124_all_wide, MTH_132_124_grades, by = "stud_id")
MTH_132_124_all_wide <- left_join(MTH_132_124_EOC_wide, MTH_132_124_pre_survey, by = "stud_id")
MTH_132_124_all_wide <- left_join(MTH_132_124_all_wide, MTH_132_124_post_survey, by = "stud_id")
MTH_132_124_all_wide <- left_join (MTH_132_124_all_wide, MTH_132_124_demographics, by = "stud_id")
MTH_132_124_all_wide <- left_join (MTH_132_124_all_wide, MTH_132_124_achievement, by = "stud_id")
MTH_132_124_all_wide <- left_join (MTH_132_124_all_wide, MTH_132_124_grades, by = "stud_id")Descriptive Stuff
MTH_132_124_EOC %>% group_by(stud_id) %>% summarise(count=n())## `summarise()` ungrouping output (override with `.groups` argument)## # A tibble: 429 x 2
## stud_id count
## <chr> <int>
## 1 124_1 6
## 2 124_10 4
## 3 124_100 6
## 4 124_101 7
## 5 124_102 6
## 6 124_103 2
## 7 124_104 9
## 8 124_105 1
## 9 124_106 5
## 10 124_108 1
## # … with 419 more rowsvariable creation
MTH_132_124_all_wide$Credits_by_two <- ntile(MTH_132_124_all_wide$Msu_Lt_Atmpt_Hours, 2)
#table(MTH_132_124_all_wide$Credits_by_two, MTH_132_124_all_wide$Msu_Lt_Atmpt_Hours)
MTH_132_124_all_wide$credits_more_than_15 <- ifelse(MTH_132_124_all_wide$Msu_Lt_Atmpt_Hours >= 15, 1, 0)
#table(MTH_132_124_all_wide$credits_more_than_15)
MTH_132_124_all_wide$hs_prep <- composite_mean_maker(MTH_132_124_all_wide, pre_hs_prep_1, pre_hs_prep_2, pre_hs_prep_3, pre_hs_prep_4, pre_hs_prep_5)
# library(psy)
# hs_prep_reliability <- select(MTH_132_124_pre_survey, pre_hs_prep_1, pre_hs_prep_2, pre_hs_prep_3, pre_hs_prep_4, pre_hs_prep_5)
# cronbach(hs_prep_reliability)Centering variables for multilevel analysis
#Group mean centering
MTH_132_124_all$eoc_future_goals_gmc <- group.center(MTH_132_124_all$eoc_future_goals, MTH_132_124_all$stud_id)Centering for linear models
MTH_132_124_all_wide$pre_val_overall_z <- scale(MTH_132_124_all_wide$pre_val_overall) %>% as.numeric()
MTH_132_124_all_wide$pre_exp_overall_z <- scale(MTH_132_124_all_wide$pre_exp_overall) %>% as.numeric()
MTH_132_124_all_wide$hs_prep_z <- scale(MTH_132_124_all_wide$hs_prep) %>% as.numeric()
MTH_132_124_all_wide$pre_hours_work_z <- scale(MTH_132_124_all_wide$pre_hours_work) %>% as.numeric()
MTH_132_124_all_wide$pre_hours_math_prep_z <- scale(MTH_132_124_all_wide$pre_hours_math_prep) %>% as.numeric()
MTH_132_124_all_wide$pre_stem_int_z <- scale(MTH_132_124_all_wide$pre_stem_int) %>% as.numeric()
MTH_132_124_all_wide$Msu_Lt_Atmpt_Hours_z <- scale(MTH_132_124_all_wide$Msu_Lt_Atmpt_Hours) %>% as.numeric()
MTH_132_124_all_wide$Best_MPS_z <- scale(MTH_132_124_all_wide$Best_MPS) %>% as.numeric()Joining data for demographics
MTH_132_124_APS_demo <- left_join(MTH_132_124_EOC, MTH_132_124_demographics, by = "stud_id")Sample Demographics Race, Gender, Class
MTH_132_124_set<-distinct(MTH_132_124_APS_demo, stud_id, gender, ethnicity)
table(MTH_132_124_set$gender)##
## F M
## 160 269table(MTH_132_124_set$ethnicity)##
## Asian (non-Hispanic)
## 27
## Black or African American (non-Hispanic)
## 20
## Hispanic Ethnicity
## 20
## International
## 85
## Not Reported
## 1
## Two or more races (non-Hispanic)
## 9
## White (non-Hispanic)
## 267table(MTH_132_124_set$stud_id)##
## 124_1 124_10 124_100 124_101 124_102 124_103 124_104 124_105 124_106 124_108
## 1 1 1 1 1 1 1 1 1 1
## 124_109 124_11 124_110 124_111 124_113 124_114 124_118 124_119 124_12 124_121
## 1 1 1 1 1 1 1 1 1 1
## 124_123 124_126 124_128 124_13 124_130 124_131 124_132 124_133 124_134 124_135
## 1 1 1 1 1 1 1 1 1 1
## 124_136 124_137 124_139 124_14 124_140 124_141 124_142 124_144 124_145 124_147
## 1 1 1 1 1 1 1 1 1 1
## 124_149 124_151 124_153 124_155 124_156 124_157 124_159 124_16 124_160 124_161
## 1 1 1 1 1 1 1 1 1 1
## 124_162 124_163 124_166 124_167 124_169 124_17 124_171 124_172 124_173 124_175
## 1 1 1 1 1 1 1 1 1 1
## 124_176 124_178 124_18 124_181 124_183 124_184 124_187 124_188 124_189 124_191
## 1 1 1 1 1 1 1 1 1 1
## 124_192 124_193 124_197 124_198 124_199 124_2 124_20 124_202 124_205 124_206
## 1 1 1 1 1 1 1 1 1 1
## 124_210 124_212 124_215 124_216 124_217 124_218 124_219 124_22 124_221 124_224
## 1 1 1 1 1 1 1 1 1 1
## 124_226 124_227 124_228 124_230 124_231 124_232 124_233 124_235 124_236 124_237
## 1 1 1 1 1 1 1 1 1 1
## 124_238 124_239 124_24 124_240 124_27 124_28 124_3 124_30 124_31 124_32
## 1 1 1 1 1 1 1 1 1 1
## 124_36 124_37 124_38 124_4 124_40 124_44 124_46 124_47 124_48 124_5
## 1 1 1 1 1 1 1 1 1 1
## 124_50 124_51 124_52 124_53 124_55 124_56 124_57 124_58 124_6 124_60
## 1 1 1 1 1 1 1 1 1 1
## 124_61 124_63 124_65 124_66 124_67 124_68 124_69 124_70 124_73 124_74
## 1 1 1 1 1 1 1 1 1 1
## 124_76 124_78 124_80 124_81 124_83 124_84 124_85 124_87 124_88 124_9
## 1 1 1 1 1 1 1 1 1 1
## 124_90 124_92 124_93 124_94 124_97 124_99 132_10 132_100 132_103 132_104
## 1 1 1 1 1 1 1 1 1 1
## 132_108 132_112 132_113 132_117 132_118 132_122 132_123 132_126 132_128 132_13
## 1 1 1 1 1 1 1 1 1 1
## 132_130 132_133 132_136 132_139 132_141 132_143 132_144 132_152 132_153 132_154
## 1 1 1 1 1 1 1 1 1 1
## 132_155 132_157 132_16 132_161 132_163 132_166 132_169 132_174 132_175 132_176
## 1 1 1 1 1 1 1 1 1 1
## 132_178 132_179 132_18 132_181 132_182 132_184 132_186 132_189 132_190 132_196
## 1 1 1 1 1 1 1 1 1 1
## 132_197 132_2 132_20 132_201 132_204 132_208 132_210 132_211 132_213 132_214
## 1 1 1 1 1 1 1 1 1 1
## 132_217 132_22 132_224 132_226 132_234 132_239 132_24 132_241 132_242 132_243
## 1 1 1 1 1 1 1 1 1 1
## 132_249 132_25 132_250 132_256 132_259 132_263 132_266 132_269 132_27 132_273
## 1 1 1 1 1 1 1 1 1 1
## 132_276 132_279 132_283 132_286 132_287 132_291 132_293 132_299 132_305 132_307
## 1 1 1 1 1 1 1 1 1 1
## 132_312 132_313 132_316 132_318 132_324 132_325 132_329 132_331 132_333 132_334
## 1 1 1 1 1 1 1 1 1 1
## 132_336 132_34 132_35 132_350 132_353 132_355 132_356 132_360 132_365 132_37
## 1 1 1 1 1 1 1 1 1 1
## 132_372 132_375 132_377 132_378 132_38 132_381 132_384 132_387 132_39 132_390
## 1 1 1 1 1 1 1 1 1 1
## 132_391 132_394 132_396 132_399 132_4 132_404 132_41 132_411 132_416 132_420
## 1 1 1 1 1 1 1 1 1 1
## 132_421 132_423 132_425 132_427 132_428 132_43 132_431 132_434 132_436 132_438
## 1 1 1 1 1 1 1 1 1 1
## 132_440 132_441 132_443 132_444 132_447 132_459 132_46 132_460 132_462 132_463
## 1 1 1 1 1 1 1 1 1 1
## 132_465 132_466 132_467 132_469 132_471 132_478 132_480 132_482 132_488 132_49
## 1 1 1 1 1 1 1 1 1 1
## 132_493 132_495 132_500 132_502 132_504 132_507 132_51 132_513 132_515 132_517
## 1 1 1 1 1 1 1 1 1 1
## 132_518 132_520 132_523 132_524 132_526 132_527 132_53 132_530 132_533 132_534
## 1 1 1 1 1 1 1 1 1 1
## 132_535 132_536 132_543 132_546 132_548 132_549 132_555 132_556 132_560 132_57
## 1 1 1 1 1 1 1 1 1 1
## 132_570 132_575 132_578 132_579 132_584 132_586 132_587 132_589 132_597 132_601
## 1 1 1 1 1 1 1 1 1 1
## 132_604 132_61 132_610 132_612 132_614 132_62 132_627 132_63 132_631 132_637
## 1 1 1 1 1 1 1 1 1 1
## 132_638 132_639 132_64 132_640 132_642 132_645 132_646 132_647 132_650 132_652
## 1 1 1 1 1 1 1 1 1 1
## 132_656 132_658 132_661 132_668 132_671 132_672 132_675 132_676 132_677 132_678
## 1 1 1 1 1 1 1 1 1 1
## 132_679 132_680 132_681 132_682 132_687 132_689 132_691 132_696 132_70 132_703
## 1 1 1 1 1 1 1 1 1 1
## 132_707 132_710 132_711 132_712 132_714 132_718 132_719 132_72 132_721 132_724
## 1 1 1 1 1 1 1 1 1 1
## 132_725 132_727 132_728 132_729 132_731 132_732 132_740 132_743 132_745 132_747
## 1 1 1 1 1 1 1 1 1 1
## 132_748 132_749 132_750 132_751 132_758 132_761 132_762 132_764 132_769 132_770
## 1 1 1 1 1 1 1 1 1 1
## 132_772 132_778 132_8 132_82 132_86 132_87 132_89 132_92 132_98
## 1 1 1 1 1 1 1 1 1MTH_132_124_set %>% group_by(stud_id) %>% summarise(count=n())## `summarise()` ungrouping output (override with `.groups` argument)## # A tibble: 429 x 2
## stud_id count
## <chr> <int>
## 1 124_1 1
## 2 124_10 1
## 3 124_100 1
## 4 124_101 1
## 5 124_102 1
## 6 124_103 1
## 7 124_104 1
## 8 124_105 1
## 9 124_106 1
## 10 124_108 1
## # … with 419 more rowsDescriptives
# a<-left_join(MTH_132_124_set, MTH_132_124_pre_survey, by="stud_id")
# describe(a$pre_cost_te_overall)
# describe(a$pre_cost_oe_overall)
# describe(a$pre_cost_lv_overall)
# describe(a$pre_cost_em_overall)
# describe(a$pre_exp_overall)
# describe(a$pre_val_overall)
#
# b<-left_join(MTH_132_124_set, MTH_132_124_post_survey, by = "stud_id")
# describe(b$post_stem_int)
#
# d<-left_join(b, MTH_132_124_achievement)
# describe(d$Grade)
#
# describe(MTH_132_124_EOC$eoc_cost_te)
# describe(MTH_132_124_EOC$eoc_cost_oe)
# describe(MTH_132_124_EOC$eoc_cost_lv)
# describe(MTH_132_124_EOC$eoc_cost_em)APS Class Level frequencies
# table(a$pre_class_code)
# table(a$pre_class_level)APS reliabilities
# library(psy)
#
# pre_te <- select(a, pre_cost_te_1, pre_cost_te_2, pre_cost_te_3, pre_cost_te_4, pre_cost_te_5)
# cronbach(pre_te)
#
# pre_oe <- select(a, pre_cost_oe_1, pre_cost_oe_2, pre_cost_oe_3, pre_cost_oe_4)
# cronbach(pre_oe)
#
# pre_lv <- select(a, pre_cost_lv_1, pre_cost_lv_2, pre_cost_lv_3, pre_cost_lv_4)
# cronbach(pre_lv)
#
# pre_em <- select(a, pre_cost_em_1, pre_cost_em_2, pre_cost_em_3, pre_cost_em_4, pre_cost_em_5, pre_cost_em_6)
# cronbach(pre_em)
#
# pre_exp <-select(a, pre_exp_1, pre_exp_2, pre_exp_3)
# cronbach(pre_exp)
#
# pre_val <-select(a, pre_val_1, pre_val_2, pre_val_3)
# cronbach(pre_val)Within person correlations
# library(rmcorr)
# MTH_132_124_EOC$stud_id <- as.factor(MTH_132_124_EOC$stud_id)
# out <- rmcorr(participant = stud_id, eoc_cost_lv, eoc_cost_em, data = MTH_132_124_EOC)
# outBetween person correlations
# MTH_132_124_all %>% group_by(stud_id) %>%
# select(eoc_cost_te, eoc_cost_oe, eoc_cost_lv, eoc_cost_em, pre_cost_te_overall, pre_cost_oe_overall, pre_cost_lv_overall, pre_cost_em_overall, pre_exp_overall, pre_val_overall, Grade, post_stem_int) %>%
# summarize_all(mean) %>%
# select(-stud_id) %>%
# as.matrix() %>%
# Hmisc::rcorr()Response Rate across participants in 52%
# library(psych)
# c <- count(MTH_132_124_EOC, stud_id)
# c <- rename(c, signals_responded_to = n)
# c$response_rate <- (c$signals_responded_to/11)
# describe(c$response_rate)
# describe(c$signals_responded_to)
#
# d<-left_join (c, MTH_132_124_pre_survey, by = "stud_id")ANOVA for response rate
# rr_anov <- aov(response_rate ~ pre_course, data = d)
# summary(rr_anov)
#
# d %>%
# group_by(pre_course) %>%
# summarise(mean=mean(response_rate))MANOVA for pre and post cost
# e<-left_join(a, b, by="stud_id")
# e<-left_join(e, MTH_132_124_achievement, by="stud_id")
#
# cost_man<-manova(cbind(pre_cost_te_overall, pre_cost_oe_overall, pre_cost_lv_overall, pre_cost_em_overall,
# pre_exp_overall, pre_val_overall, Grade, post_stem_int)
# ~ pre_course, data = e)
# summary(cost_man, test = "Wilks")
# summary.aov(cost_man)
#
# e %>%
# group_by(pre_course) %>%
# summarise_at(vars(post_stem_int), funs(mean(., na.rm=TRUE)))MANOVA for EOC cost variables
# eoc_cost_man<-manova(cbind(eoc_cost_te, eoc_cost_oe, eoc_cost_lv, eoc_cost_em)
# ~ pre_course, data = MTH_132_124_all)
# summary(eoc_cost_man, test = "Wilks")
# summary.aov(eoc_cost_man)
#
# MTH_132_124_all %>%
# group_by(pre_course) %>%
# summarise_at(vars(eoc_cost_te), funs(mean(., na.rm=TRUE)))
#
# MTH_132_124_all %>%
# group_by(pre_course) %>%
# summarise_at(vars(eoc_cost_oe), funs(mean(., na.rm=TRUE)))
#
# MTH_132_124_all %>%
# group_by(pre_course) %>%
# summarise_at(vars(eoc_cost_lv), funs(mean(., na.rm=TRUE)))
#
# MTH_132_124_all %>%
# group_by(pre_course) %>%
# summarise_at(vars(eoc_cost_em), funs(mean(., na.rm=TRUE)))Creating variables for missing
# MTH_132_124_EOC$missing_frustrated <- is.na(MTH_132_124_EOC$frustrated)
# table(MTH_132_124_EOC$missing_frustrated)
# MTH_132_124_EOC$missing_bored <- is.na(MTH_132_124_EOC$bored)
# table(MTH_132_124_EOC$missing_bored)
# MTH_132_124_EOC$missing_happy <- is.na(MTH_132_124_EOC$happy)
# table(MTH_132_124_EOC$missing_happy)
# MTH_132_124_EOC$missing_excited <- is.na(MTH_132_124_EOC$excited)
# table(MTH_132_124_EOC$missing_excited)
# MTH_132_124_EOC$missing_control <- is.na(MTH_132_124_EOC$overall_control)
# table(MTH_132_124_EOC$missing_control)
# MTH_132_124_EOC$missing_value <- is.na(MTH_132_124_EOC$overall_value)
# table(MTH_132_124_EOC$missing_value)
# e$missing_grade <- is.na (e$Grade)
# table(e$missing_grade)
# e$missing_post_stem_int <- is.na (e$post_stem_int)
# table(e$missing_post_stem_int)Missing Data Analysis
# #joining for missing data analysis
# f<- left_join(d, MTH_132_124_demographics, by = "stud_id")
#
# #checking response rate
# t.test(f$response_rate ~ f$female)
#
# fit<-aov(f$response_rate ~ f$ethnicity)
# summary(fit)
# TukeyHSD(fit)
#
# fit2<-aov(f$response_rate ~ f$pre_class_code)
# summary(fit2)
#
# t.test(f$response_rate ~ f$pre_course)
#
# #Chisquare on grades
# chisq.test(e$missing_grade, e$ethnicity.x)
# chisq.test(e$missing_grade, e$ethnicity.x, simulate.p.value = TRUE)
#
# chisq.test(e$missing_grade, e$gender.x)
# chisq.test(e$missing_grade, e$gender.x, simulate.p.value = TRUE)
#
# chisq.test(e$missing_grade, e$pre_class_code)
# chisq.test(e$missing_grade, e$pre_class_code, simulate.p.value = TRUE)
#
# chisq.test(e$missing_grade, e$pre_course)
# chisq.test(e$missing_grade, e$pre_course, simulate.p.value = TRUE)
#
# #Chisquare on stemint
# chisq.test(e$missing_post_stem_int, e$ethnicity.x)
# chisq.test(e$missing_post_stem_int, e$ethnicity.x, simulate.p.value = TRUE)
#
# chisq.test(e$missing_post_stem_int, e$gender.x)
# chisq.test(e$missing_post_stem_int, e$gender.x, simulate.p.value = TRUE)
#
# chisq.test(e$missing_post_stem_int, e$pre_class_code)
# chisq.test(e$missing_post_stem_int, e$pre_class_code, simulate.p.value = TRUE)
#
# chisq.test(e$missing_post_stem_int, e$pre_course)
# chisq.test(e$missing_post_stem_int, e$pre_course, simulate.p.value = TRUE)# table1 <- table(e$missing_grade, e$ethnicity.x)
# table1
# #round(prop.table(table1), 2)
# #round(prop.table(table1, 1), 2)
#
# table2 <- table(e$missing_grade, e$pre_class_code)
# table2
#
# table3 <- table(e$missing_post_stem_int, e$ethnicity.x)
# table3
#
# table4 <- table(e$missing_post_stem_int, e$pre_class_code)
# table4
#
# table5 <- table(e$missing_post_stem_int, e$pre_course)
# table5
#
# f %>%
# group_by(ethnicity) %>%
# summarise_at(vars(response_rate), funs(mean(., na.rm=TRUE)))M00 <- lm(pre_cost_te_overall ~ pre_val_overall_z + pre_exp_overall_z + hs_prep_z +
pre_hours_work_z + pre_hours_math_prep_z + pre_stem_int_z + credits_more_than_15 +
female + urm + Best_MPS_z,
data = MTH_132_124_all_wide)
summary(M00)##
## Call:
## lm(formula = pre_cost_te_overall ~ pre_val_overall_z + pre_exp_overall_z +
## hs_prep_z + pre_hours_work_z + pre_hours_math_prep_z + pre_stem_int_z +
## credits_more_than_15 + female + urm + Best_MPS_z, data = MTH_132_124_all_wide)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.6029 -0.8030 -0.1224 0.6586 3.0225
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.78801 0.17248 21.962 < 2e-16 ***
## pre_val_overall_z 0.02579 0.14410 0.179 0.858255
## pre_exp_overall_z -0.55743 0.14800 -3.767 0.000248 ***
## hs_prep_z -0.01327 0.10057 -0.132 0.895243
## pre_hours_work_z 0.21925 0.11752 1.866 0.064304 .
## pre_hours_math_prep_z 0.35145 0.09881 3.557 0.000522 ***
## pre_stem_int_z -0.17789 0.15811 -1.125 0.262592
## credits_more_than_15 -0.40073 0.19681 -2.036 0.043743 *
## female -0.15591 0.19071 -0.818 0.415114
## urm -0.04410 0.33298 -0.132 0.894831
## Best_MPS_z -0.15880 0.13881 -1.144 0.254685
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.084 on 132 degrees of freedom
## (286 observations deleted due to missingness)
## Multiple R-squared: 0.2699, Adjusted R-squared: 0.2146
## F-statistic: 4.88 on 10 and 132 DF, p-value: 5.523e-06M11 <- lm(pre_cost_oe_overall ~ pre_val_overall_z + pre_exp_overall_z + hs_prep_z +
pre_hours_work_z + pre_hours_math_prep_z + pre_stem_int_z + credits_more_than_15 +
female + urm + Best_MPS_z,
data = MTH_132_124_all_wide)
summary(M11)##
## Call:
## lm(formula = pre_cost_oe_overall ~ pre_val_overall_z + pre_exp_overall_z +
## hs_prep_z + pre_hours_work_z + pre_hours_math_prep_z + pre_stem_int_z +
## credits_more_than_15 + female + urm + Best_MPS_z, data = MTH_132_124_all_wide)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.6672 -0.6895 -0.1783 0.3689 4.4178
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.15746 0.17074 18.492 <2e-16 ***
## pre_val_overall_z -0.08194 0.14265 -0.574 0.5667
## pre_exp_overall_z -0.39650 0.14651 -2.706 0.0077 **
## hs_prep_z -0.08236 0.09956 -0.827 0.4096
## pre_hours_work_z 0.11217 0.11633 0.964 0.3367
## pre_hours_math_prep_z 0.08755 0.09781 0.895 0.3724
## pre_stem_int_z -0.26681 0.15652 -1.705 0.0906 .
## credits_more_than_15 -0.22862 0.19484 -1.173 0.2428
## female -0.18407 0.18879 -0.975 0.3314
## urm -0.11610 0.32963 -0.352 0.7253
## Best_MPS_z -0.06661 0.13741 -0.485 0.6286
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.073 on 132 degrees of freedom
## (286 observations deleted due to missingness)
## Multiple R-squared: 0.2013, Adjusted R-squared: 0.1408
## F-statistic: 3.326 on 10 and 132 DF, p-value: 0.0006982M22 <- lm(pre_cost_lv_overall ~ pre_val_overall_z + pre_exp_overall_z + hs_prep_z +
pre_hours_work_z + pre_hours_math_prep_z + pre_stem_int_z + credits_more_than_15 +
female + urm + Best_MPS_z,
data = MTH_132_124_all_wide)
summary(M22)##
## Call:
## lm(formula = pre_cost_lv_overall ~ pre_val_overall_z + pre_exp_overall_z +
## hs_prep_z + pre_hours_work_z + pre_hours_math_prep_z + pre_stem_int_z +
## credits_more_than_15 + female + urm + Best_MPS_z, data = MTH_132_124_all_wide)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.5118 -0.6965 -0.0837 0.5152 3.6499
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.60566 0.17166 21.005 < 2e-16 ***
## pre_val_overall_z -0.03004 0.14341 -0.209 0.834398
## pre_exp_overall_z -0.47137 0.14729 -3.200 0.001720 **
## hs_prep_z 0.07979 0.10009 0.797 0.426760
## pre_hours_work_z 0.11367 0.11696 0.972 0.332893
## pre_hours_math_prep_z 0.33577 0.09834 3.414 0.000849 ***
## pre_stem_int_z -0.05879 0.15736 -0.374 0.709281
## credits_more_than_15 -0.60138 0.19588 -3.070 0.002598 **
## female -0.03325 0.18980 -0.175 0.861202
## urm 0.57545 0.33140 1.736 0.084817 .
## Best_MPS_z -0.21753 0.13815 -1.575 0.117744
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.079 on 132 degrees of freedom
## (286 observations deleted due to missingness)
## Multiple R-squared: 0.2594, Adjusted R-squared: 0.2033
## F-statistic: 4.623 on 10 and 132 DF, p-value: 1.224e-05M33 <- lm(pre_cost_em_overall ~ pre_val_overall_z + pre_exp_overall_z + hs_prep_z +
pre_hours_work_z + pre_hours_math_prep_z + pre_stem_int_z + credits_more_than_15 +
female + urm + Best_MPS_z,
data = MTH_132_124_all_wide)
summary(M33)##
## Call:
## lm(formula = pre_cost_em_overall ~ pre_val_overall_z + pre_exp_overall_z +
## hs_prep_z + pre_hours_work_z + pre_hours_math_prep_z + pre_stem_int_z +
## credits_more_than_15 + female + urm + Best_MPS_z, data = MTH_132_124_all_wide)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.5032 -0.8251 -0.1313 0.7993 3.3309
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.03269 0.18108 22.270 < 2e-16 ***
## pre_val_overall_z 0.02828 0.15129 0.187 0.851988
## pre_exp_overall_z -0.57240 0.15538 -3.684 0.000334 ***
## hs_prep_z -0.05631 0.10558 -0.533 0.594733
## pre_hours_work_z 0.23140 0.12338 1.876 0.062928 .
## pre_hours_math_prep_z 0.30355 0.10374 2.926 0.004042 **
## pre_stem_int_z -0.02804 0.16600 -0.169 0.866141
## credits_more_than_15 -0.55296 0.20663 -2.676 0.008394 **
## female 0.37199 0.20022 1.858 0.065415 .
## urm 0.24907 0.34959 0.712 0.477440
## Best_MPS_z -0.43191 0.14573 -2.964 0.003607 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.138 on 132 degrees of freedom
## (286 observations deleted due to missingness)
## Multiple R-squared: 0.3102, Adjusted R-squared: 0.2579
## F-statistic: 5.936 on 10 and 132 DF, p-value: 2.21e-07M0 <- lmer(eoc_cost_te ~ pre_cost_te_overall_z + pre_val_overall_z + pre_exp_overall_z + hs_prep_z +
pre_hours_work_z + pre_hours_math_prep + pre_stem_int_z + credits_more_than_15 + week +
female + urm + Best_MPS_z + eoc_future_goals_gmc +
(1|stud_id),
data = MTH_132_124_all, control=lmerControl(optimizer="bobyqa"))
summary(M0)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## eoc_cost_te ~ pre_cost_te_overall_z + pre_val_overall_z + pre_exp_overall_z +
## hs_prep_z + pre_hours_work_z + pre_hours_math_prep + pre_stem_int_z +
## credits_more_than_15 + week + female + urm + Best_MPS_z +
## eoc_future_goals_gmc + (1 | stud_id)
## Data: MTH_132_124_all
## Control: lmerControl(optimizer = "bobyqa")
##
## REML criterion at convergence: 1978.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4802 -0.5636 -0.0533 0.5772 3.3686
##
## Random effects:
## Groups Name Variance Std.Dev.
## stud_id (Intercept) 0.9299 0.9643
## Residual 0.8374 0.9151
## Number of obs: 651, groups: stud_id, 136
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.929029 0.317441 130.683092 9.227 6.37e-16 ***
## pre_cost_te_overall_z 0.757032 0.111968 129.846231 6.761 4.20e-10 ***
## pre_val_overall_z -0.189236 0.150314 121.212763 -1.259 0.210472
## pre_exp_overall_z -0.058511 0.167891 135.710146 -0.349 0.728000
## hs_prep_z -0.253142 0.107819 130.057040 -2.348 0.020391 *
## pre_hours_work_z -0.092375 0.125869 127.361162 -0.734 0.464358
## pre_hours_math_prep -0.007682 0.081006 119.957615 -0.095 0.924603
## pre_stem_int_z 0.008285 0.155080 125.216848 0.053 0.957481
## credits_more_than_15 0.060535 0.215287 123.675612 0.281 0.779040
## week 0.042576 0.012856 574.298998 3.312 0.000985 ***
## female 0.048115 0.199723 121.642465 0.241 0.810033
## urm 0.929445 0.389015 136.587986 2.389 0.018250 *
## Best_MPS_z -0.307212 0.142934 122.877412 -2.149 0.033569 *
## eoc_future_goals_gmc -0.048478 0.037352 522.143463 -1.298 0.194909
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 14 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need itperformance::icc(M0, by_group = T)## # ICC by Group
##
## Group | ICC
## ---------------
## stud_id | 0.526M1 <- lmer(eoc_cost_oe ~ pre_cost_oe_overall_z + pre_val_overall_z + pre_exp_overall_z + hs_prep_z +
pre_hours_work_z + pre_hours_math_prep + pre_stem_int_z + credits_more_than_15 + week +
female + urm + Best_MPS_z + eoc_future_goals_gmc +
(1|stud_id),
data = MTH_132_124_all, control=lmerControl(optimizer="bobyqa"))
summary(M1)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## eoc_cost_oe ~ pre_cost_oe_overall_z + pre_val_overall_z + pre_exp_overall_z +
## hs_prep_z + pre_hours_work_z + pre_hours_math_prep + pre_stem_int_z +
## credits_more_than_15 + week + female + urm + Best_MPS_z +
## eoc_future_goals_gmc + (1 | stud_id)
## Data: MTH_132_124_all
## Control: lmerControl(optimizer = "bobyqa")
##
## REML criterion at convergence: 1971.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9073 -0.5331 -0.0792 0.5292 4.4393
##
## Random effects:
## Groups Name Variance Std.Dev.
## stud_id (Intercept) 0.8370 0.9149
## Residual 0.8382 0.9155
## Number of obs: 652, groups: stud_id, 136
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.28709 0.30260 131.32617 7.558 6.23e-12 ***
## pre_cost_oe_overall_z 0.63499 0.09462 115.35525 6.711 7.59e-10 ***
## pre_val_overall_z -0.05537 0.14457 119.74842 -0.383 0.7024
## pre_exp_overall_z -0.15813 0.15756 134.70584 -1.004 0.3174
## hs_prep_z -0.17389 0.10364 128.42083 -1.678 0.0958 .
## pre_hours_work_z -0.11435 0.11914 123.42091 -0.960 0.3390
## pre_hours_math_prep 0.08130 0.07453 117.33151 1.091 0.2776
## pre_stem_int_z 0.02243 0.14981 123.62313 0.150 0.8812
## credits_more_than_15 0.27119 0.20393 120.83686 1.330 0.1861
## week 0.03004 0.01282 577.96010 2.343 0.0194 *
## female 0.17610 0.19169 120.27263 0.919 0.3601
## urm 0.67796 0.37426 136.22435 1.811 0.0723 .
## Best_MPS_z -0.21372 0.13667 121.38643 -1.564 0.1205
## eoc_future_goals_gmc -0.01647 0.03736 523.24370 -0.441 0.6594
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 14 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need itperformance::icc(M1, by_group = T)## # ICC by Group
##
## Group | ICC
## ---------------
## stud_id | 0.500M2 <- lmer(eoc_cost_lv ~ pre_cost_lv_overall_z + pre_val_overall_z + pre_exp_overall_z + hs_prep_z +
pre_hours_work_z + pre_hours_math_prep + pre_stem_int_z + credits_more_than_15 + week +
female + urm + Best_MPS_z + eoc_future_goals_gmc +
(1|stud_id),
data = MTH_132_124_all, control=lmerControl(optimizer="bobyqa"))
summary(M2)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## eoc_cost_lv ~ pre_cost_lv_overall_z + pre_val_overall_z + pre_exp_overall_z +
## hs_prep_z + pre_hours_work_z + pre_hours_math_prep + pre_stem_int_z +
## credits_more_than_15 + week + female + urm + Best_MPS_z +
## eoc_future_goals_gmc + (1 | stud_id)
## Data: MTH_132_124_all
## Control: lmerControl(optimizer = "bobyqa")
##
## REML criterion at convergence: 1978.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.6871 -0.5230 -0.0506 0.4428 5.2804
##
## Random effects:
## Groups Name Variance Std.Dev.
## stud_id (Intercept) 1.0713 1.0350
## Residual 0.8124 0.9013
## Number of obs: 652, groups: stud_id, 136
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.299193 0.333509 129.037290 6.894 2.17e-10 ***
## pre_cost_lv_overall_z 0.465606 0.111155 125.463868 4.189 5.24e-05 ***
## pre_val_overall_z -0.122208 0.158668 121.174164 -0.770 0.44267
## pre_exp_overall_z -0.245721 0.174535 133.013030 -1.408 0.16150
## hs_prep_z -0.279825 0.113695 128.451881 -2.461 0.01518 *
## pre_hours_work_z -0.002589 0.130823 123.902491 -0.020 0.98424
## pre_hours_math_prep 0.128090 0.085106 118.636984 1.505 0.13497
## pre_stem_int_z -0.059956 0.162828 124.270399 -0.368 0.71334
## credits_more_than_15 0.243173 0.230231 122.980642 1.056 0.29294
## week 0.032965 0.012694 568.411151 2.597 0.00965 **
## female 0.131491 0.210530 120.946302 0.625 0.53343
## urm 0.010813 0.412412 134.303459 0.026 0.97912
## Best_MPS_z -0.314107 0.151676 122.443441 -2.071 0.04047 *
## eoc_future_goals_gmc -0.104251 0.036778 520.635324 -2.835 0.00477 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 14 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need itperformance::icc(M2, by_group = T)## # ICC by Group
##
## Group | ICC
## ---------------
## stud_id | 0.569M3 <- lmer(eoc_cost_em ~ pre_cost_em_overall_z + pre_val_overall_z + pre_exp_overall_z + hs_prep_z +
pre_hours_work_z + pre_hours_math_prep + pre_stem_int_z + credits_more_than_15 + week +
female + urm + Best_MPS_z + eoc_future_goals_gmc +
(1|stud_id),
data = MTH_132_124_all, control=lmerControl(optimizer="bobyqa"))
summary(M3)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## eoc_cost_em ~ pre_cost_em_overall_z + pre_val_overall_z + pre_exp_overall_z +
## hs_prep_z + pre_hours_work_z + pre_hours_math_prep + pre_stem_int_z +
## credits_more_than_15 + week + female + urm + Best_MPS_z +
## eoc_future_goals_gmc + (1 | stud_id)
## Data: MTH_132_124_all
## Control: lmerControl(optimizer = "bobyqa")
##
## REML criterion at convergence: 2034.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.7223 -0.4916 -0.1016 0.5546 3.7470
##
## Random effects:
## Groups Name Variance Std.Dev.
## stud_id (Intercept) 1.2121 1.1009
## Residual 0.8861 0.9413
## Number of obs: 651, groups: stud_id, 136
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.069474 0.356004 128.852241 8.622 2.06e-14 ***
## pre_cost_em_overall_z 0.596850 0.124362 121.007901 4.799 4.59e-06 ***
## pre_val_overall_z -0.162990 0.168249 120.022143 -0.969 0.33462
## pre_exp_overall_z -0.087502 0.186393 130.428615 -0.469 0.63953
## hs_prep_z -0.238157 0.120578 127.823900 -1.975 0.05041 .
## pre_hours_work_z -0.276228 0.139830 124.518124 -1.975 0.05043 .
## pre_hours_math_prep -0.031048 0.090162 119.050388 -0.344 0.73119
## pre_stem_int_z -0.238120 0.172441 122.860093 -1.381 0.16982
## credits_more_than_15 0.180425 0.240593 119.819090 0.750 0.45477
## week 0.009297 0.013283 565.791235 0.700 0.48427
## female 0.291815 0.226535 120.573313 1.288 0.20015
## urm 1.339101 0.435899 133.122465 3.072 0.00258 **
## Best_MPS_z -0.226512 0.164166 120.623084 -1.380 0.17021
## eoc_future_goals_gmc -0.040233 0.038424 518.408150 -1.047 0.29555
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 14 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need itperformance::icc(M3, by_group = T)## # ICC by Group
##
## Group | ICC
## ---------------
## stud_id | 0.578M4 <- lm(post_cost_te_overall ~ pre_cost_te_overall + pre_val_overall + pre_exp_overall +
pre_hours_work + Msu_Lt_Atmpt_Hours + female,
data = MTH_132_124_pre_post)
summary(M4)##
## Call:
## lm(formula = post_cost_te_overall ~ pre_cost_te_overall + pre_val_overall +
## pre_exp_overall + pre_hours_work + Msu_Lt_Atmpt_Hours + female,
## data = MTH_132_124_pre_post)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.8270 -0.8402 -0.0618 0.7284 3.9596
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.389747 0.620638 5.462 6.72e-08 ***
## pre_cost_te_overall 0.475465 0.043538 10.921 < 2e-16 ***
## pre_val_overall -0.004834 0.058889 -0.082 0.93461
## pre_exp_overall -0.180782 0.066151 -2.733 0.00645 **
## pre_hours_work 0.005853 0.032567 0.180 0.85744
## Msu_Lt_Atmpt_Hours -0.036728 0.032357 -1.135 0.25675
## female 0.003566 0.098644 0.036 0.97117
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.22 on 650 degrees of freedom
## (365 observations deleted due to missingness)
## Multiple R-squared: 0.2299, Adjusted R-squared: 0.2228
## F-statistic: 32.34 on 6 and 650 DF, p-value: < 2.2e-16cost_te <- '
#measurement model
#regressions
#direct effects
post_cost_te_overall ~ pre_cost_te_overall + pre_val_overall + pre_exp_overall + pre_hours_work + Msu_Lt_Atmpt_Hours + female
#residual correlations
'
fit <- sem(cost_te, data=MTH_132_124_pre_post, missing = "ML.x")
summary(fit, fit.measures = TRUE, standardized = TRUE)## lavaan 0.6-7 ended normally after 27 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 8
##
## Number of observations 1022
## Number of missing patterns 9
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Model Test Baseline Model:
##
## Test statistic 172.345
## Degrees of freedom 6
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.000
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) NA
## Loglikelihood unrestricted model (H1) NA
##
## Akaike (AIC) NA
## Bayesian (BIC) NA
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.000
## P-value RMSEA <= 0.05 NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_cost_te_overall ~
## pre_cst_t_vrll 0.476 0.043 11.020 0.000 0.476 0.421
## pre_val_overll -0.005 0.059 -0.093 0.926 -0.005 -0.004
## pre_exp_overll -0.181 0.066 -2.749 0.006 -0.181 -0.131
## pre_hours_work 0.003 0.032 0.080 0.936 0.003 0.003
## Ms_Lt_Atmpt_Hr -0.043 0.031 -1.370 0.171 -0.043 -0.058
## female -0.001 0.097 -0.015 0.988 -0.001 -0.001
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cst_t_vrll 3.496 0.608 5.752 0.000 3.496 2.507
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cst_t_vrll 1.472 0.081 18.226 0.000 1.472 0.757standardizedsolution(fit)## lhs op rhs est.std se z pvalue
## 1 post_cost_te_overall ~ pre_cost_te_overall 0.421 0.034 12.341 0.000
## 2 post_cost_te_overall ~ pre_val_overall -0.004 0.045 -0.093 0.926
## 3 post_cost_te_overall ~ pre_exp_overall -0.131 0.047 -2.767 0.006
## 4 post_cost_te_overall ~ pre_hours_work 0.003 0.036 0.080 0.936
## 5 post_cost_te_overall ~ Msu_Lt_Atmpt_Hours -0.058 0.042 -1.376 0.169
## 6 post_cost_te_overall ~ female -0.001 0.034 -0.015 0.988
## 7 post_cost_te_overall ~~ post_cost_te_overall 0.757 0.028 26.797 0.000
## 8 pre_cost_te_overall ~~ pre_cost_te_overall 1.000 0.000 NA NA
## 9 pre_cost_te_overall ~~ pre_val_overall -0.222 0.000 NA NA
## 10 pre_cost_te_overall ~~ pre_exp_overall -0.380 0.000 NA NA
## 11 pre_cost_te_overall ~~ pre_hours_work 0.062 0.000 NA NA
## 12 pre_cost_te_overall ~~ Msu_Lt_Atmpt_Hours -0.038 0.000 NA NA
## 13 pre_cost_te_overall ~~ female 0.035 0.000 NA NA
## 14 pre_val_overall ~~ pre_val_overall 1.000 0.000 NA NA
## 15 pre_val_overall ~~ pre_exp_overall 0.592 0.000 NA NA
## 16 pre_val_overall ~~ pre_hours_work -0.106 0.000 NA NA
## 17 pre_val_overall ~~ Msu_Lt_Atmpt_Hours 0.002 0.000 NA NA
## 18 pre_val_overall ~~ female 0.022 0.000 NA NA
## 19 pre_exp_overall ~~ pre_exp_overall 1.000 0.000 NA NA
## 20 pre_exp_overall ~~ pre_hours_work -0.033 0.000 NA NA
## 21 pre_exp_overall ~~ Msu_Lt_Atmpt_Hours -0.009 0.000 NA NA
## 22 pre_exp_overall ~~ female -0.067 0.000 NA NA
## 23 pre_hours_work ~~ pre_hours_work 1.000 0.000 NA NA
## 24 pre_hours_work ~~ Msu_Lt_Atmpt_Hours -0.087 0.000 NA NA
## 25 pre_hours_work ~~ female 0.050 0.000 NA NA
## 26 Msu_Lt_Atmpt_Hours ~~ Msu_Lt_Atmpt_Hours 1.000 0.000 NA NA
## 27 Msu_Lt_Atmpt_Hours ~~ female 0.037 0.000 NA NA
## 28 female ~~ female 1.000 0.000 NA NA
## 29 post_cost_te_overall ~1 2.507 0.437 5.733 0.000
## 30 pre_cost_te_overall ~1 2.731 0.000 NA NA
## 31 pre_val_overall ~1 5.246 0.000 NA NA
## 32 pre_exp_overall ~1 5.541 0.000 NA NA
## 33 pre_hours_work ~1 1.420 0.000 NA NA
## 34 Msu_Lt_Atmpt_Hours ~1 7.324 0.000 NA NA
## 35 female ~1 0.752 0.000 NA NA
## ci.lower ci.upper
## 1 0.354 0.488
## 2 -0.092 0.084
## 3 -0.224 -0.038
## 4 -0.067 0.073
## 5 -0.141 0.025
## 6 -0.066 0.065
## 7 0.701 0.812
## 8 1.000 1.000
## 9 -0.222 -0.222
## 10 -0.380 -0.380
## 11 0.062 0.062
## 12 -0.038 -0.038
## 13 0.035 0.035
## 14 1.000 1.000
## 15 0.592 0.592
## 16 -0.106 -0.106
## 17 0.002 0.002
## 18 0.022 0.022
## 19 1.000 1.000
## 20 -0.033 -0.033
## 21 -0.009 -0.009
## 22 -0.067 -0.067
## 23 1.000 1.000
## 24 -0.087 -0.087
## 25 0.050 0.050
## 26 1.000 1.000
## 27 0.037 0.037
## 28 1.000 1.000
## 29 1.650 3.364
## 30 2.731 2.731
## 31 5.246 5.246
## 32 5.541 5.541
## 33 1.420 1.420
## 34 7.324 7.324
## 35 0.752 0.752M5 <- lm(post_cost_oe_overall ~ pre_cost_oe_overall + pre_val_overall + pre_exp_overall +
pre_hours_work + Msu_Lt_Atmpt_Hours + female,
data = MTH_132_124_pre_post)
summary(M5)##
## Call:
## lm(formula = post_cost_oe_overall ~ pre_cost_oe_overall + pre_val_overall +
## pre_exp_overall + pre_hours_work + Msu_Lt_Atmpt_Hours + female,
## data = MTH_132_124_pre_post)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.3800 -0.8148 -0.0594 0.8241 3.9708
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.384874 0.600460 3.972 7.93e-05 ***
## pre_cost_oe_overall 0.493515 0.046755 10.555 < 2e-16 ***
## pre_val_overall 0.023283 0.056299 0.414 0.6793
## pre_exp_overall -0.126268 0.062712 -2.013 0.0445 *
## pre_hours_work 0.042864 0.031087 1.379 0.1684
## Msu_Lt_Atmpt_Hours -0.008668 0.030953 -0.280 0.7795
## female 0.002671 0.094457 0.028 0.9775
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.167 on 650 degrees of freedom
## (365 observations deleted due to missingness)
## Multiple R-squared: 0.1998, Adjusted R-squared: 0.1924
## F-statistic: 27.05 on 6 and 650 DF, p-value: < 2.2e-16cost_oe <- '
#measurement model
#regressions
#direct effects
post_cost_oe_overall ~ pre_cost_oe_overall + pre_val_overall + pre_exp_overall + pre_hours_work + Msu_Lt_Atmpt_Hours + female
#residual correlations
'
fit <- sem(cost_oe, data=MTH_132_124_pre_post, missing = "ML.x")
summary(fit, fit.measures = TRUE, standardized = TRUE)## lavaan 0.6-7 ended normally after 27 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 8
##
## Number of observations 1022
## Number of missing patterns 9
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Model Test Baseline Model:
##
## Test statistic 145.626
## Degrees of freedom 6
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.000
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) NA
## Loglikelihood unrestricted model (H1) NA
##
## Akaike (AIC) NA
## Bayesian (BIC) NA
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.000
## P-value RMSEA <= 0.05 NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_cost_oe_overall ~
## pre_cost__vrll 0.492 0.047 10.580 0.000 0.492 0.417
## pre_val_overll 0.022 0.056 0.391 0.696 0.022 0.018
## pre_exp_overll -0.128 0.063 -2.040 0.041 -0.128 -0.099
## pre_hours_work 0.040 0.031 1.296 0.195 0.040 0.047
## Ms_Lt_Atmpt_Hr -0.021 0.030 -0.703 0.482 -0.021 -0.030
## female -0.010 0.094 -0.112 0.911 -0.010 -0.004
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_cst__vrll 2.601 0.589 4.414 0.000 2.601 1.980
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .post_cst__vrll 1.355 0.074 18.218 0.000 1.355 0.785standardizedsolution(fit)## lhs op rhs est.std se z pvalue
## 1 post_cost_oe_overall ~ pre_cost_oe_overall 0.417 0.035 11.876 0.000
## 2 post_cost_oe_overall ~ pre_val_overall 0.018 0.046 0.391 0.696
## 3 post_cost_oe_overall ~ pre_exp_overall -0.099 0.048 -2.047 0.041
## 4 post_cost_oe_overall ~ pre_hours_work 0.047 0.036 1.300 0.194
## 5 post_cost_oe_overall ~ Msu_Lt_Atmpt_Hours -0.030 0.043 -0.703 0.482
## 6 post_cost_oe_overall ~ female -0.004 0.034 -0.112 0.911
## 7 post_cost_oe_overall ~~ post_cost_oe_overall 0.785 0.028 27.999 0.000
## 8 pre_cost_oe_overall ~~ pre_cost_oe_overall 1.000 0.000 NA NA
## 9 pre_cost_oe_overall ~~ pre_val_overall -0.246 0.000 NA NA
## 10 pre_cost_oe_overall ~~ pre_exp_overall -0.371 0.000 NA NA
## 11 pre_cost_oe_overall ~~ pre_hours_work 0.042 0.000 NA NA
## 12 pre_cost_oe_overall ~~ Msu_Lt_Atmpt_Hours -0.032 0.000 NA NA
## 13 pre_cost_oe_overall ~~ female -0.023 0.000 NA NA
## 14 pre_val_overall ~~ pre_val_overall 1.000 0.000 NA NA
## 15 pre_val_overall ~~ pre_exp_overall 0.592 0.000 NA NA
## 16 pre_val_overall ~~ pre_hours_work -0.107 0.000 NA NA
## 17 pre_val_overall ~~ Msu_Lt_Atmpt_Hours 0.002 0.000 NA NA
## 18 pre_val_overall ~~ female 0.022 0.000 NA NA
## 19 pre_exp_overall ~~ pre_exp_overall 1.000 0.000 NA NA
## 20 pre_exp_overall ~~ pre_hours_work -0.033 0.000 NA NA
## 21 pre_exp_overall ~~ Msu_Lt_Atmpt_Hours -0.009 0.000 NA NA
## 22 pre_exp_overall ~~ female -0.066 0.000 NA NA
## 23 pre_hours_work ~~ pre_hours_work 1.000 0.000 NA NA
## 24 pre_hours_work ~~ Msu_Lt_Atmpt_Hours -0.087 0.000 NA NA
## 25 pre_hours_work ~~ female 0.049 0.000 NA NA
## 26 Msu_Lt_Atmpt_Hours ~~ Msu_Lt_Atmpt_Hours 1.000 0.000 NA NA
## 27 Msu_Lt_Atmpt_Hours ~~ female 0.037 0.000 NA NA
## 28 female ~~ female 1.000 0.000 NA NA
## 29 post_cost_oe_overall ~1 1.980 0.453 4.369 0.000
## 30 pre_cost_oe_overall ~1 2.539 0.000 NA NA
## 31 pre_val_overall ~1 5.245 0.000 NA NA
## 32 pre_exp_overall ~1 5.541 0.000 NA NA
## 33 pre_hours_work ~1 1.420 0.000 NA NA
## 34 Msu_Lt_Atmpt_Hours ~1 7.324 0.000 NA NA
## 35 female ~1 0.752 0.000 NA NA
## ci.lower ci.upper
## 1 0.348 0.486
## 2 -0.072 0.107
## 3 -0.193 -0.004
## 4 -0.024 0.119
## 5 -0.115 0.054
## 6 -0.071 0.063
## 7 0.730 0.840
## 8 1.000 1.000
## 9 -0.246 -0.246
## 10 -0.371 -0.371
## 11 0.042 0.042
## 12 -0.032 -0.032
## 13 -0.023 -0.023
## 14 1.000 1.000
## 15 0.592 0.592
## 16 -0.107 -0.107
## 17 0.002 0.002
## 18 0.022 0.022
## 19 1.000 1.000
## 20 -0.033 -0.033
## 21 -0.009 -0.009
## 22 -0.066 -0.066
## 23 1.000 1.000
## 24 -0.087 -0.087
## 25 0.049 0.049
## 26 1.000 1.000
## 27 0.037 0.037
## 28 1.000 1.000
## 29 1.092 2.868
## 30 2.539 2.539
## 31 5.245 5.245
## 32 5.541 5.541
## 33 1.420 1.420
## 34 7.324 7.324
## 35 0.752 0.752M6 <- lm(post_cost_lv_overall ~ pre_cost_lv_overall + pre_val_overall + pre_exp_overall +
pre_hours_work + Msu_Lt_Atmpt_Hours + female,
data = MTH_132_124_pre_post)
summary(M6)##
## Call:
## lm(formula = post_cost_lv_overall ~ pre_cost_lv_overall + pre_val_overall +
## pre_exp_overall + pre_hours_work + Msu_Lt_Atmpt_Hours + female,
## data = MTH_132_124_pre_post)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.3916 -0.8353 -0.0204 0.7154 4.4884
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.16539 0.59672 5.305 1.55e-07 ***
## pre_cost_lv_overall 0.44510 0.04327 10.287 < 2e-16 ***
## pre_val_overall 0.02240 0.05634 0.398 0.691034
## pre_exp_overall -0.21239 0.06231 -3.408 0.000694 ***
## pre_hours_work 0.02541 0.03111 0.817 0.414354
## Msu_Lt_Atmpt_Hours -0.02248 0.03097 -0.726 0.468143
## female -0.06188 0.09457 -0.654 0.513114
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.168 on 651 degrees of freedom
## (364 observations deleted due to missingness)
## Multiple R-squared: 0.2129, Adjusted R-squared: 0.2056
## F-statistic: 29.34 on 6 and 651 DF, p-value: < 2.2e-16cost_lv <- '
#measurement model
#regressions
#direct effects
post_cost_lv_overall ~ pre_cost_lv_overall + pre_val_overall + pre_exp_overall + pre_hours_work + Msu_Lt_Atmpt_Hours + female
#residual correlations
'
fit <- sem(cost_lv, data=MTH_132_124_pre_post, missing = "ML.x")
summary(fit, fit.measures = TRUE, standardized = TRUE)## lavaan 0.6-7 ended normally after 26 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 8
##
## Number of observations 1022
## Number of missing patterns 9
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Model Test Baseline Model:
##
## Test statistic 156.878
## Degrees of freedom 6
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.000
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) NA
## Loglikelihood unrestricted model (H1) NA
##
## Akaike (AIC) NA
## Bayesian (BIC) NA
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.000
## P-value RMSEA <= 0.05 NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_cost_lv_overall ~
## pr_cst_lv_vrll 0.440 0.043 10.285 0.000 0.440 0.392
## pre_val_overll 0.020 0.056 0.366 0.714 0.020 0.017
## pre_exp_overll -0.213 0.062 -3.437 0.001 -0.213 -0.163
## pre_hours_work 0.021 0.031 0.669 0.504 0.021 0.024
## Ms_Lt_Atmpt_Hr -0.036 0.030 -1.183 0.237 -0.036 -0.051
## female -0.068 0.093 -0.729 0.466 -0.068 -0.025
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cst_lv_vrl 3.395 0.582 5.837 0.000 3.395 2.575
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cst_lv_vrl 1.348 0.074 18.257 0.000 1.348 0.775standardizedsolution(fit)## lhs op rhs est.std se z pvalue
## 1 post_cost_lv_overall ~ pre_cost_lv_overall 0.392 0.034 11.386 0.000
## 2 post_cost_lv_overall ~ pre_val_overall 0.017 0.045 0.366 0.714
## 3 post_cost_lv_overall ~ pre_exp_overall -0.163 0.047 -3.477 0.001
## 4 post_cost_lv_overall ~ pre_hours_work 0.024 0.036 0.669 0.503
## 5 post_cost_lv_overall ~ Msu_Lt_Atmpt_Hours -0.051 0.043 -1.187 0.235
## 6 post_cost_lv_overall ~ female -0.025 0.034 -0.729 0.466
## 7 post_cost_lv_overall ~~ post_cost_lv_overall 0.775 0.028 27.633 0.000
## 8 pre_cost_lv_overall ~~ pre_cost_lv_overall 1.000 0.000 NA NA
## 9 pre_cost_lv_overall ~~ pre_val_overall -0.194 0.000 NA NA
## 10 pre_cost_lv_overall ~~ pre_exp_overall -0.346 0.000 NA NA
## 11 pre_cost_lv_overall ~~ pre_hours_work 0.031 0.000 NA NA
## 12 pre_cost_lv_overall ~~ Msu_Lt_Atmpt_Hours -0.039 0.000 NA NA
## 13 pre_cost_lv_overall ~~ female -0.036 0.000 NA NA
## 14 pre_val_overall ~~ pre_val_overall 1.000 0.000 NA NA
## 15 pre_val_overall ~~ pre_exp_overall 0.592 0.000 NA NA
## 16 pre_val_overall ~~ pre_hours_work -0.106 0.000 NA NA
## 17 pre_val_overall ~~ Msu_Lt_Atmpt_Hours 0.002 0.000 NA NA
## 18 pre_val_overall ~~ female 0.022 0.000 NA NA
## 19 pre_exp_overall ~~ pre_exp_overall 1.000 0.000 NA NA
## 20 pre_exp_overall ~~ pre_hours_work -0.033 0.000 NA NA
## 21 pre_exp_overall ~~ Msu_Lt_Atmpt_Hours -0.009 0.000 NA NA
## 22 pre_exp_overall ~~ female -0.067 0.000 NA NA
## 23 pre_hours_work ~~ pre_hours_work 1.000 0.000 NA NA
## 24 pre_hours_work ~~ Msu_Lt_Atmpt_Hours -0.087 0.000 NA NA
## 25 pre_hours_work ~~ female 0.050 0.000 NA NA
## 26 Msu_Lt_Atmpt_Hours ~~ Msu_Lt_Atmpt_Hours 1.000 0.000 NA NA
## 27 Msu_Lt_Atmpt_Hours ~~ female 0.037 0.000 NA NA
## 28 female ~~ female 1.000 0.000 NA NA
## 29 post_cost_lv_overall ~1 2.575 0.443 5.812 0.000
## 30 pre_cost_lv_overall ~1 2.710 0.000 NA NA
## 31 pre_val_overall ~1 5.246 0.000 NA NA
## 32 pre_exp_overall ~1 5.542 0.000 NA NA
## 33 pre_hours_work ~1 1.420 0.000 NA NA
## 34 Msu_Lt_Atmpt_Hours ~1 7.324 0.000 NA NA
## 35 female ~1 0.752 0.000 NA NA
## ci.lower ci.upper
## 1 0.324 0.459
## 2 -0.072 0.105
## 3 -0.255 -0.071
## 4 -0.047 0.095
## 5 -0.135 0.033
## 6 -0.091 0.042
## 7 0.720 0.830
## 8 1.000 1.000
## 9 -0.194 -0.194
## 10 -0.346 -0.346
## 11 0.031 0.031
## 12 -0.039 -0.039
## 13 -0.036 -0.036
## 14 1.000 1.000
## 15 0.592 0.592
## 16 -0.106 -0.106
## 17 0.002 0.002
## 18 0.022 0.022
## 19 1.000 1.000
## 20 -0.033 -0.033
## 21 -0.009 -0.009
## 22 -0.067 -0.067
## 23 1.000 1.000
## 24 -0.087 -0.087
## 25 0.050 0.050
## 26 1.000 1.000
## 27 0.037 0.037
## 28 1.000 1.000
## 29 1.707 3.443
## 30 2.710 2.710
## 31 5.246 5.246
## 32 5.542 5.542
## 33 1.420 1.420
## 34 7.324 7.324
## 35 0.752 0.752M7 <- lm(post_cost_em_overall ~ pre_cost_em_overall + pre_val_overall + pre_exp_overall +
pre_hours_work + Msu_Lt_Atmpt_Hours + female,
data = MTH_132_124_pre_post)
summary(M7)##
## Call:
## lm(formula = post_cost_em_overall ~ pre_cost_em_overall + pre_val_overall +
## pre_exp_overall + pre_hours_work + Msu_Lt_Atmpt_Hours + female,
## data = MTH_132_124_pre_post)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.1118 -0.8967 -0.0224 0.8378 4.0928
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.50933 0.66635 5.267 1.89e-07 ***
## pre_cost_em_overall 0.55485 0.04447 12.478 < 2e-16 ***
## pre_val_overall -0.05130 0.06228 -0.824 0.410
## pre_exp_overall -0.16055 0.07098 -2.262 0.024 *
## pre_hours_work 0.02122 0.03432 0.618 0.537
## Msu_Lt_Atmpt_Hours -0.04549 0.03415 -1.332 0.183
## female 0.09635 0.10467 0.921 0.358
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.288 on 649 degrees of freedom
## (366 observations deleted due to missingness)
## Multiple R-squared: 0.2823, Adjusted R-squared: 0.2757
## F-statistic: 42.55 on 6 and 649 DF, p-value: < 2.2e-16cost_em <- '
#measurement model
#regressions
#direct effects
post_cost_em_overall ~ pre_cost_em_overall + pre_val_overall + pre_exp_overall + pre_hours_work + Msu_Lt_Atmpt_Hours + female
#residual correlations
'
fit <- sem(cost_em, data=MTH_132_124_pre_post, missing = "ML.x")
summary(fit, fit.measures = TRUE, standardized = TRUE)## lavaan 0.6-7 ended normally after 27 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 8
##
## Number of observations 1022
## Number of missing patterns 9
##
## Model Test User Model:
##
## Test statistic 0.000
## Degrees of freedom 0
##
## Model Test Baseline Model:
##
## Test statistic 218.644
## Degrees of freedom 6
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 1.000
## Tucker-Lewis Index (TLI) 1.000
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) NA
## Loglikelihood unrestricted model (H1) NA
##
## Akaike (AIC) NA
## Bayesian (BIC) NA
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.000
## 90 Percent confidence interval - lower 0.000
## 90 Percent confidence interval - upper 0.000
## P-value RMSEA <= 0.05 NA
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Observed
## Observed information based on Hessian
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## post_cost_em_overall ~
## pre_cst_m_vrll 0.550 0.044 12.547 0.000 0.550 0.469
## pre_val_overll -0.053 0.062 -0.855 0.393 -0.053 -0.037
## pre_exp_overll -0.161 0.070 -2.288 0.022 -0.161 -0.107
## pre_hours_work 0.022 0.034 0.665 0.506 0.022 0.023
## Ms_Lt_Atmpt_Hr -0.053 0.033 -1.610 0.107 -0.053 -0.066
## female 0.102 0.103 0.989 0.322 0.102 0.032
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cst_m_vrll 3.646 0.649 5.620 0.000 3.646 2.393
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pst_cst_m_vrll 1.631 0.089 18.228 0.000 1.631 0.703standardizedsolution(fit)## lhs op rhs est.std se z pvalue
## 1 post_cost_em_overall ~ pre_cost_em_overall 0.469 0.033 14.384 0.000
## 2 post_cost_em_overall ~ pre_val_overall -0.037 0.043 -0.855 0.392
## 3 post_cost_em_overall ~ pre_exp_overall -0.107 0.046 -2.298 0.022
## 4 post_cost_em_overall ~ pre_hours_work 0.023 0.034 0.666 0.505
## 5 post_cost_em_overall ~ Msu_Lt_Atmpt_Hours -0.066 0.041 -1.618 0.106
## 6 post_cost_em_overall ~ female 0.032 0.033 0.990 0.322
## 7 post_cost_em_overall ~~ post_cost_em_overall 0.703 0.029 24.629 0.000
## 8 pre_cost_em_overall ~~ pre_cost_em_overall 1.000 0.000 NA NA
## 9 pre_cost_em_overall ~~ pre_val_overall -0.190 0.000 NA NA
## 10 pre_cost_em_overall ~~ pre_exp_overall -0.402 0.000 NA NA
## 11 pre_cost_em_overall ~~ pre_hours_work 0.021 0.000 NA NA
## 12 pre_cost_em_overall ~~ Msu_Lt_Atmpt_Hours -0.047 0.000 NA NA
## 13 pre_cost_em_overall ~~ female 0.115 0.000 NA NA
## 14 pre_val_overall ~~ pre_val_overall 1.000 0.000 NA NA
## 15 pre_val_overall ~~ pre_exp_overall 0.591 0.000 NA NA
## 16 pre_val_overall ~~ pre_hours_work -0.107 0.000 NA NA
## 17 pre_val_overall ~~ Msu_Lt_Atmpt_Hours 0.002 0.000 NA NA
## 18 pre_val_overall ~~ female 0.021 0.000 NA NA
## 19 pre_exp_overall ~~ pre_exp_overall 1.000 0.000 NA NA
## 20 pre_exp_overall ~~ pre_hours_work -0.034 0.000 NA NA
## 21 pre_exp_overall ~~ Msu_Lt_Atmpt_Hours -0.010 0.000 NA NA
## 22 pre_exp_overall ~~ female -0.068 0.000 NA NA
## 23 pre_hours_work ~~ pre_hours_work 1.000 0.000 NA NA
## 24 pre_hours_work ~~ Msu_Lt_Atmpt_Hours -0.087 0.000 NA NA
## 25 pre_hours_work ~~ female 0.050 0.000 NA NA
## 26 Msu_Lt_Atmpt_Hours ~~ Msu_Lt_Atmpt_Hours 1.000 0.000 NA NA
## 27 Msu_Lt_Atmpt_Hours ~~ female 0.037 0.000 NA NA
## 28 female ~~ female 1.000 0.000 NA NA
## 29 post_cost_em_overall ~1 2.393 0.428 5.598 0.000
## 30 pre_cost_em_overall ~1 2.861 0.000 NA NA
## 31 pre_val_overall ~1 5.246 0.000 NA NA
## 32 pre_exp_overall ~1 5.542 0.000 NA NA
## 33 pre_hours_work ~1 1.420 0.000 NA NA
## 34 Msu_Lt_Atmpt_Hours ~1 7.324 0.000 NA NA
## 35 female ~1 0.752 0.000 NA NA
## ci.lower ci.upper
## 1 0.405 0.532
## 2 -0.121 0.048
## 3 -0.197 -0.016
## 4 -0.045 0.091
## 5 -0.145 0.014
## 6 -0.032 0.096
## 7 0.647 0.759
## 8 1.000 1.000
## 9 -0.190 -0.190
## 10 -0.402 -0.402
## 11 0.021 0.021
## 12 -0.047 -0.047
## 13 0.115 0.115
## 14 1.000 1.000
## 15 0.591 0.591
## 16 -0.107 -0.107
## 17 0.002 0.002
## 18 0.021 0.021
## 19 1.000 1.000
## 20 -0.034 -0.034
## 21 -0.010 -0.010
## 22 -0.068 -0.068
## 23 1.000 1.000
## 24 -0.087 -0.087
## 25 0.050 0.050
## 26 1.000 1.000
## 27 0.037 0.037
## 28 1.000 1.000
## 29 1.555 3.231
## 30 2.861 2.861
## 31 5.246 5.246
## 32 5.542 5.542
## 33 1.420 1.420
## 34 7.324 7.324
## 35 0.752 0.752Task Effort
cost_te <-'
#Measurement
hs_prep_1 =~ pre_hs_prep_1 + pre_hs_prep_2 + pre_hs_prep_3 + pre_hs_prep_4 + pre_hs_prep_5
expect =~ pre_exp_1 + pre_exp_2 + pre_exp_3
value =~ pre_val_1 + pre_val_2 + pre_val_3
a_te_cost =~ pre_cost_te_1 + pre_cost_te_2 + pre_cost_te_3 + pre_cost_te_4 + pre_cost_te_5
#Regressions
a_te_cost ~ aa*hs_prep_1 + ab*pre_hours_work + ac*pre_hours_math_prep + ad*credits_more_than_15 + ae*pre_stem_int + af*expect + ag*value + ah*female + ai*urm + aj*Best_MPS
eoc_cost_te ~ b*a_te_cost + hs_prep_1 + pre_hours_work + pre_hours_math_prep + credits_more_than_15 + pre_stem_int + expect + value + female + urm + Best_MPS + week
#indirect effects
hs_cost:= aa*b
work_cost:= ab*b
prep_cost:= ac*b
credits_cost:= ad*b
stem_cost:= ae*b
expect_cost:= af*b
val_cost:= ag*b
female_cost:= ah*b
urm_cost:= ai*b
mps_cost:= aj*b
#Covariances
# pre_stem_int ~~ expect + value + a_te_cost
# expect ~~ value + a_te_cost
# value ~~ a_te_cost
# pre_hours_math_prep ~~ pre_hours_math_prep
'
fit <- sem(cost_te, data=MTH_132_124_all, estimator = "MLR", cluster = "stud_id", missing = "ML.x", se = 'bootstrap')
test = parameterEstimates(fit,boot.ci.type = 'bca.simple',level=.95) # bootstrapped estimate
summary(fit, fit.measures = TRUE, standardized = TRUE)## lavaan 0.6-7 ended normally after 93 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 75
##
## Number of observations 2435
## Number of clusters [stud_id] 429
## Number of missing patterns 41
##
## Model Test User Model:
## Standard Robust
## Test Statistic 2358.604 331.562
## Degrees of freedom 231 231
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 7.114
## Yuan-Bentler correction (Mplus variant)
##
## Model Test Baseline Model:
##
## Test statistic 20873.117 2602.117
## Degrees of freedom 272 272
## P-value 0.000 0.000
## Scaling correction factor 8.022
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.897 0.957
## Tucker-Lewis Index (TLI) 0.878 0.949
##
## Robust Comparative Fit Index (CFI) 0.962
## Robust Tucker-Lewis Index (TLI) 0.955
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) NA NA
## Scaling correction factor 11.494
## for the MLR correction
## Loglikelihood unrestricted model (H1) NA NA
## Scaling correction factor 8.187
## for the MLR correction
##
## Akaike (AIC) NA NA
## Bayesian (BIC) NA NA
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.062 0.013
## 90 Percent confidence interval - lower 0.059 0.012
## 90 Percent confidence interval - upper 0.064 0.015
## P-value RMSEA <= 0.05 0.000 1.000
##
## Robust RMSEA 0.036
## 90 Percent confidence interval - lower 0.027
## 90 Percent confidence interval - upper 0.044
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.066 0.066
##
## Parameter Estimates:
##
## Standard errors Robust.cluster
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## hs_prep_1 =~
## pre_hs_prep_1 1.000 0.682 0.519
## pre_hs_prep_2 0.736 0.108 6.797 0.000 0.502 0.614
## pre_hs_prep_3 0.759 0.118 6.439 0.000 0.518 0.784
## pre_hs_prep_4 0.802 0.126 6.375 0.000 0.547 0.841
## pre_hs_prep_5 0.793 0.148 5.347 0.000 0.541 0.652
## expect =~
## pre_exp_1 1.000 0.884 0.763
## pre_exp_2 0.913 0.078 11.704 0.000 0.807 0.827
## pre_exp_3 1.129 0.080 14.183 0.000 0.998 0.831
## value =~
## pre_val_1 1.000 1.026 0.843
## pre_val_2 0.955 0.053 18.104 0.000 0.980 0.852
## pre_val_3 1.047 0.053 19.871 0.000 1.074 0.845
## a_te_cost =~
## pre_cost_te_1 1.000 1.175 0.842
## pre_cost_te_2 0.908 0.063 14.384 0.000 1.067 0.735
## pre_cost_te_3 0.990 0.048 20.551 0.000 1.163 0.849
## pre_cost_te_4 1.032 0.060 17.086 0.000 1.212 0.826
## pre_cost_te_5 1.009 0.062 16.408 0.000 1.186 0.827
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## a_te_cost ~
## hs_prep_1 (aa) -0.295 0.232 -1.272 0.203 -0.172 -0.172
## pr_hrs_wr (ab) 0.014 0.057 0.245 0.806 0.012 0.018
## pr_hrs_m_ (ac) 0.097 0.059 1.656 0.098 0.083 0.109
## crdt___15 (ad) -0.287 0.128 -2.234 0.025 -0.244 -0.117
## pr_stm_nt (ae) -0.025 0.040 -0.619 0.536 -0.021 -0.039
## expect (af) -0.485 0.181 -2.684 0.007 -0.365 -0.365
## value (ag) 0.019 0.107 0.178 0.859 0.017 0.017
## female (ah) 0.048 0.134 0.357 0.721 0.041 0.020
## urm (ai) 0.099 0.224 0.441 0.659 0.084 0.023
## Best_MPS (aj) 0.006 0.013 0.457 0.647 0.005 0.026
## eoc_cost_te ~
## a_te_cost (b) 0.521 0.075 6.985 0.000 0.612 0.379
## hs_prep_1 -0.269 0.150 -1.792 0.073 -0.183 -0.114
## pr_hrs_wr 0.046 0.046 0.997 0.319 0.046 0.043
## pr_hrs_m_ 0.097 0.055 1.787 0.074 0.097 0.080
## crdt___15 -0.324 0.134 -2.417 0.016 -0.324 -0.097
## pr_stm_nt -0.067 0.042 -1.587 0.113 -0.067 -0.076
## expect -0.306 0.146 -2.091 0.037 -0.270 -0.167
## value 0.171 0.126 1.357 0.175 0.176 0.109
## female -0.102 0.128 -0.793 0.428 -0.102 -0.031
## urm 0.360 0.240 1.500 0.134 0.360 0.060
## Best_MPS -0.044 0.014 -3.041 0.002 -0.044 -0.137
## week 0.006 0.011 0.564 0.573 0.006 0.012
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## hs_prep_1 ~~
## expect 0.223 0.090 2.467 0.014 0.370 0.370
## value 0.186 0.100 1.857 0.063 0.266 0.266
## expect ~~
## value 0.614 0.130 4.725 0.000 0.677 0.677
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pre_hs_prep_1 4.349 0.109 39.945 0.000 4.349 3.307
## .pre_hs_prep_2 4.928 0.071 69.238 0.000 4.928 6.025
## .pre_hs_prep_3 5.303 0.056 95.061 0.000 5.303 8.029
## .pre_hs_prep_4 5.439 0.057 94.766 0.000 5.439 8.360
## .pre_hs_prep_5 5.128 0.069 74.039 0.000 5.128 6.171
## .pre_exp_1 5.716 0.066 86.114 0.000 5.716 4.933
## .pre_exp_2 5.923 0.057 103.303 0.000 5.923 6.071
## .pre_exp_3 5.496 0.069 79.317 0.000 5.496 4.579
## .pre_val_1 5.805 0.071 81.510 0.000 5.805 4.768
## .pre_val_2 5.659 0.067 84.337 0.000 5.659 4.921
## .pre_val_3 5.576 0.075 74.571 0.000 5.576 4.385
## .pre_cost_te_1 3.080 0.441 6.977 0.000 3.080 2.207
## .pre_cost_te_2 3.272 0.405 8.082 0.000 3.272 2.254
## .pre_cost_te_3 2.934 0.438 6.704 0.000 2.934 2.141
## .pre_cost_te_4 3.161 0.453 6.972 0.000 3.161 2.155
## .pre_cost_te_5 3.045 0.447 6.809 0.000 3.045 2.123
## .eoc_cost_te 3.806 0.456 8.339 0.000 3.806 2.357
## hs_prep_1 0.000 0.000 0.000
## expect 0.000 0.000 0.000
## value 0.000 0.000 0.000
## .a_te_cost 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pre_hs_prep_1 1.264 0.178 7.116 0.000 1.264 0.731
## .pre_hs_prep_2 0.417 0.068 6.110 0.000 0.417 0.623
## .pre_hs_prep_3 0.168 0.033 5.026 0.000 0.168 0.386
## .pre_hs_prep_4 0.124 0.028 4.357 0.000 0.124 0.292
## .pre_hs_prep_5 0.397 0.148 2.687 0.007 0.397 0.575
## .pre_exp_1 0.561 0.159 3.532 0.000 0.561 0.418
## .pre_exp_2 0.300 0.060 4.964 0.000 0.300 0.315
## .pre_exp_3 0.445 0.086 5.176 0.000 0.445 0.309
## .pre_val_1 0.429 0.095 4.512 0.000 0.429 0.290
## .pre_val_2 0.363 0.060 6.079 0.000 0.363 0.274
## .pre_val_3 0.462 0.086 5.366 0.000 0.462 0.286
## .pre_cost_te_1 0.567 0.092 6.141 0.000 0.567 0.291
## .pre_cost_te_2 0.969 0.119 8.158 0.000 0.969 0.460
## .pre_cost_te_3 0.526 0.072 7.291 0.000 0.526 0.280
## .pre_cost_te_4 0.683 0.127 5.372 0.000 0.683 0.317
## .pre_cost_te_5 0.650 0.101 6.407 0.000 0.650 0.316
## .eoc_cost_te 1.826 0.124 14.736 0.000 1.826 0.701
## hs_prep_1 0.466 0.125 3.730 0.000 1.000 1.000
## expect 0.782 0.159 4.924 0.000 1.000 1.000
## value 1.053 0.185 5.692 0.000 1.000 1.000
## .a_te_cost 1.065 0.145 7.323 0.000 0.771 0.771
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## hs_cost -0.154 0.115 -1.338 0.181 -0.105 -0.065
## work_cost 0.007 0.030 0.241 0.810 0.007 0.007
## prep_cost 0.051 0.034 1.500 0.134 0.051 0.041
## credits_cost -0.149 0.070 -2.142 0.032 -0.149 -0.045
## stem_cost -0.013 0.021 -0.623 0.533 -0.013 -0.015
## expect_cost -0.253 0.109 -2.321 0.020 -0.223 -0.138
## val_cost 0.010 0.056 0.178 0.858 0.010 0.006
## female_cost 0.025 0.069 0.360 0.719 0.025 0.008
## urm_cost 0.052 0.116 0.443 0.658 0.052 0.009
## mps_cost 0.003 0.007 0.452 0.651 0.003 0.010test## lhs op rhs label est se
## 1 hs_prep_1 =~ pre_hs_prep_1 1.000 0.000
## 2 hs_prep_1 =~ pre_hs_prep_2 0.736 0.108
## 3 hs_prep_1 =~ pre_hs_prep_3 0.759 0.118
## 4 hs_prep_1 =~ pre_hs_prep_4 0.802 0.126
## 5 hs_prep_1 =~ pre_hs_prep_5 0.793 0.148
## 6 expect =~ pre_exp_1 1.000 0.000
## 7 expect =~ pre_exp_2 0.913 0.078
## 8 expect =~ pre_exp_3 1.129 0.080
## 9 value =~ pre_val_1 1.000 0.000
## 10 value =~ pre_val_2 0.955 0.053
## 11 value =~ pre_val_3 1.047 0.053
## 12 a_te_cost =~ pre_cost_te_1 1.000 0.000
## 13 a_te_cost =~ pre_cost_te_2 0.908 0.063
## 14 a_te_cost =~ pre_cost_te_3 0.990 0.048
## 15 a_te_cost =~ pre_cost_te_4 1.032 0.060
## 16 a_te_cost =~ pre_cost_te_5 1.009 0.062
## 17 a_te_cost ~ hs_prep_1 aa -0.295 0.232
## 18 a_te_cost ~ pre_hours_work ab 0.014 0.057
## 19 a_te_cost ~ pre_hours_math_prep ac 0.097 0.059
## 20 a_te_cost ~ credits_more_than_15 ad -0.287 0.128
## 21 a_te_cost ~ pre_stem_int ae -0.025 0.040
## 22 a_te_cost ~ expect af -0.485 0.181
## 23 a_te_cost ~ value ag 0.019 0.107
## 24 a_te_cost ~ female ah 0.048 0.134
## 25 a_te_cost ~ urm ai 0.099 0.224
## 26 a_te_cost ~ Best_MPS aj 0.006 0.013
## 27 eoc_cost_te ~ a_te_cost b 0.521 0.075
## 28 eoc_cost_te ~ hs_prep_1 -0.269 0.150
## 29 eoc_cost_te ~ pre_hours_work 0.046 0.046
## 30 eoc_cost_te ~ pre_hours_math_prep 0.097 0.055
## 31 eoc_cost_te ~ credits_more_than_15 -0.324 0.134
## 32 eoc_cost_te ~ pre_stem_int -0.067 0.042
## 33 eoc_cost_te ~ expect -0.306 0.146
## 34 eoc_cost_te ~ value 0.171 0.126
## 35 eoc_cost_te ~ female -0.102 0.128
## 36 eoc_cost_te ~ urm 0.360 0.240
## 37 eoc_cost_te ~ Best_MPS -0.044 0.014
## 38 eoc_cost_te ~ week 0.006 0.011
## 39 pre_hs_prep_1 ~~ pre_hs_prep_1 1.264 0.178
## 40 pre_hs_prep_2 ~~ pre_hs_prep_2 0.417 0.068
## 41 pre_hs_prep_3 ~~ pre_hs_prep_3 0.168 0.033
## 42 pre_hs_prep_4 ~~ pre_hs_prep_4 0.124 0.028
## 43 pre_hs_prep_5 ~~ pre_hs_prep_5 0.397 0.148
## 44 pre_exp_1 ~~ pre_exp_1 0.561 0.159
## 45 pre_exp_2 ~~ pre_exp_2 0.300 0.060
## 46 pre_exp_3 ~~ pre_exp_3 0.445 0.086
## 47 pre_val_1 ~~ pre_val_1 0.429 0.095
## 48 pre_val_2 ~~ pre_val_2 0.363 0.060
## 49 pre_val_3 ~~ pre_val_3 0.462 0.086
## 50 pre_cost_te_1 ~~ pre_cost_te_1 0.567 0.092
## 51 pre_cost_te_2 ~~ pre_cost_te_2 0.969 0.119
## 52 pre_cost_te_3 ~~ pre_cost_te_3 0.526 0.072
## 53 pre_cost_te_4 ~~ pre_cost_te_4 0.683 0.127
## 54 pre_cost_te_5 ~~ pre_cost_te_5 0.650 0.101
## 55 eoc_cost_te ~~ eoc_cost_te 1.826 0.124
## 56 hs_prep_1 ~~ hs_prep_1 0.466 0.125
## 57 expect ~~ expect 0.782 0.159
## 58 value ~~ value 1.053 0.185
## 59 a_te_cost ~~ a_te_cost 1.065 0.145
## 60 hs_prep_1 ~~ expect 0.223 0.090
## 61 hs_prep_1 ~~ value 0.186 0.100
## 62 expect ~~ value 0.614 0.130
## 63 pre_hours_work ~~ pre_hours_work 2.327 0.000
## 64 pre_hours_work ~~ pre_hours_math_prep 0.159 0.000
## 65 pre_hours_work ~~ credits_more_than_15 -0.031 0.000
## 66 pre_hours_work ~~ pre_stem_int 0.325 0.000
## 67 pre_hours_work ~~ female 0.089 0.000
## 68 pre_hours_work ~~ urm 0.087 0.000
## 69 pre_hours_work ~~ Best_MPS -0.646 0.000
## 70 pre_hours_work ~~ week -0.012 0.000
## 71 pre_hours_math_prep ~~ pre_hours_math_prep 1.744 0.000
## 72 pre_hours_math_prep ~~ credits_more_than_15 -0.033 0.000
## 73 pre_hours_math_prep ~~ pre_stem_int 0.055 0.000
## 74 pre_hours_math_prep ~~ female -0.053 0.000
## 75 pre_hours_math_prep ~~ urm -0.003 0.000
## 76 pre_hours_math_prep ~~ Best_MPS 0.475 0.000
## 77 pre_hours_math_prep ~~ week -0.082 0.000
## 78 credits_more_than_15 ~~ credits_more_than_15 0.231 0.000
## 79 credits_more_than_15 ~~ pre_stem_int -0.068 0.000
## 80 credits_more_than_15 ~~ female 0.007 0.000
## 81 credits_more_than_15 ~~ urm 0.007 0.000
## 82 credits_more_than_15 ~~ Best_MPS -0.107 0.000
## 83 credits_more_than_15 ~~ week 0.004 0.000
## 84 pre_stem_int ~~ pre_stem_int 3.382 0.000
## 85 pre_stem_int ~~ female 0.005 0.000
## 86 pre_stem_int ~~ urm 0.030 0.000
## 87 pre_stem_int ~~ Best_MPS 0.235 0.000
## 88 pre_stem_int ~~ week -0.126 0.000
## 89 female ~~ female 0.243 0.000
## 90 female ~~ urm 0.012 0.000
## 91 female ~~ Best_MPS -0.231 0.000
## 92 female ~~ week 0.077 0.000
## 93 urm ~~ urm 0.072 0.000
## 94 urm ~~ Best_MPS -0.366 0.000
## 95 urm ~~ week -0.008 0.000
## 96 Best_MPS ~~ Best_MPS 25.353 0.000
## 97 Best_MPS ~~ week -0.311 0.000
## 98 week ~~ week 9.922 0.000
## 99 pre_hs_prep_1 ~1 4.349 0.109
## 100 pre_hs_prep_2 ~1 4.928 0.071
## 101 pre_hs_prep_3 ~1 5.303 0.056
## 102 pre_hs_prep_4 ~1 5.439 0.057
## 103 pre_hs_prep_5 ~1 5.128 0.069
## 104 pre_exp_1 ~1 5.716 0.066
## 105 pre_exp_2 ~1 5.923 0.057
## 106 pre_exp_3 ~1 5.496 0.069
## 107 pre_val_1 ~1 5.805 0.071
## 108 pre_val_2 ~1 5.659 0.067
## 109 pre_val_3 ~1 5.576 0.075
## 110 pre_cost_te_1 ~1 3.080 0.441
## 111 pre_cost_te_2 ~1 3.272 0.405
## 112 pre_cost_te_3 ~1 2.934 0.438
## 113 pre_cost_te_4 ~1 3.161 0.453
## 114 pre_cost_te_5 ~1 3.045 0.447
## 115 eoc_cost_te ~1 3.806 0.456
## 116 pre_hours_work ~1 2.189 0.000
## 117 pre_hours_math_prep ~1 3.310 0.000
## 118 credits_more_than_15 ~1 0.363 0.000
## 119 pre_stem_int ~1 5.041 0.000
## 120 female ~1 0.417 0.000
## 121 urm ~1 0.078 0.000
## 122 Best_MPS ~1 18.664 0.000
## 123 week ~1 5.469 0.000
## 124 hs_prep_1 ~1 0.000 0.000
## 125 expect ~1 0.000 0.000
## 126 value ~1 0.000 0.000
## 127 a_te_cost ~1 0.000 0.000
## 128 hs_cost := aa*b hs_cost -0.154 0.115
## 129 work_cost := ab*b work_cost 0.007 0.030
## 130 prep_cost := ac*b prep_cost 0.051 0.034
## 131 credits_cost := ad*b credits_cost -0.149 0.070
## 132 stem_cost := ae*b stem_cost -0.013 0.021
## 133 expect_cost := af*b expect_cost -0.253 0.109
## 134 val_cost := ag*b val_cost 0.010 0.056
## 135 female_cost := ah*b female_cost 0.025 0.069
## 136 urm_cost := ai*b urm_cost 0.052 0.116
## 137 mps_cost := aj*b mps_cost 0.003 0.007
## z pvalue ci.lower ci.upper
## 1 NA NA 1.000 1.000
## 2 6.797 0.000 0.524 0.948
## 3 6.439 0.000 0.528 0.990
## 4 6.375 0.000 0.556 1.049
## 5 5.347 0.000 0.503 1.084
## 6 NA NA 1.000 1.000
## 7 11.704 0.000 0.760 1.066
## 8 14.183 0.000 0.973 1.285
## 9 NA NA 1.000 1.000
## 10 18.104 0.000 0.851 1.058
## 11 19.871 0.000 0.944 1.150
## 12 NA NA 1.000 1.000
## 13 14.384 0.000 0.785 1.032
## 14 20.551 0.000 0.895 1.084
## 15 17.086 0.000 0.913 1.150
## 16 16.408 0.000 0.889 1.130
## 17 -1.272 0.203 -0.750 0.160
## 18 0.245 0.806 -0.098 0.126
## 19 1.656 0.098 -0.018 0.212
## 20 -2.234 0.025 -0.539 -0.035
## 21 -0.619 0.536 -0.104 0.054
## 22 -2.684 0.007 -0.839 -0.131
## 23 0.178 0.859 -0.191 0.230
## 24 0.357 0.721 -0.215 0.311
## 25 0.441 0.659 -0.341 0.539
## 26 0.457 0.647 -0.020 0.033
## 27 6.985 0.000 0.375 0.667
## 28 -1.792 0.073 -0.563 0.025
## 29 0.997 0.319 -0.044 0.136
## 30 1.787 0.074 -0.009 0.204
## 31 -2.417 0.016 -0.587 -0.061
## 32 -1.587 0.113 -0.149 0.016
## 33 -2.091 0.037 -0.592 -0.019
## 34 1.357 0.175 -0.076 0.418
## 35 -0.793 0.428 -0.353 0.150
## 36 1.500 0.134 -0.110 0.830
## 37 -3.041 0.002 -0.072 -0.016
## 38 0.564 0.573 -0.016 0.028
## 39 7.116 0.000 0.916 1.613
## 40 6.110 0.000 0.283 0.551
## 41 5.026 0.000 0.103 0.234
## 42 4.357 0.000 0.068 0.179
## 43 2.687 0.007 0.107 0.687
## 44 3.532 0.000 0.250 0.872
## 45 4.964 0.000 0.182 0.419
## 46 5.176 0.000 0.276 0.613
## 47 4.512 0.000 0.243 0.616
## 48 6.079 0.000 0.246 0.480
## 49 5.366 0.000 0.294 0.631
## 50 6.141 0.000 0.386 0.748
## 51 8.158 0.000 0.736 1.202
## 52 7.291 0.000 0.384 0.667
## 53 5.372 0.000 0.434 0.932
## 54 6.407 0.000 0.451 0.848
## 55 14.736 0.000 1.583 2.069
## 56 3.730 0.000 0.221 0.710
## 57 4.924 0.000 0.471 1.093
## 58 5.692 0.000 0.690 1.416
## 59 7.323 0.000 0.780 1.350
## 60 2.467 0.014 0.046 0.400
## 61 1.857 0.063 -0.010 0.383
## 62 4.725 0.000 0.359 0.869
## 63 NA NA 2.327 2.327
## 64 NA NA 0.159 0.159
## 65 NA NA -0.031 -0.031
## 66 NA NA 0.325 0.325
## 67 NA NA 0.089 0.089
## 68 NA NA 0.087 0.087
## 69 NA NA -0.646 -0.646
## 70 NA NA -0.012 -0.012
## 71 NA NA 1.744 1.744
## 72 NA NA -0.033 -0.033
## 73 NA NA 0.055 0.055
## 74 NA NA -0.053 -0.053
## 75 NA NA -0.003 -0.003
## 76 NA NA 0.475 0.475
## 77 NA NA -0.082 -0.082
## 78 NA NA 0.231 0.231
## 79 NA NA -0.068 -0.068
## 80 NA NA 0.007 0.007
## 81 NA NA 0.007 0.007
## 82 NA NA -0.107 -0.107
## 83 NA NA 0.004 0.004
## 84 NA NA 3.382 3.382
## 85 NA NA 0.005 0.005
## 86 NA NA 0.030 0.030
## 87 NA NA 0.235 0.235
## 88 NA NA -0.126 -0.126
## 89 NA NA 0.243 0.243
## 90 NA NA 0.012 0.012
## 91 NA NA -0.231 -0.231
## 92 NA NA 0.077 0.077
## 93 NA NA 0.072 0.072
## 94 NA NA -0.366 -0.366
## 95 NA NA -0.008 -0.008
## 96 NA NA 25.353 25.353
## 97 NA NA -0.311 -0.311
## 98 NA NA 9.922 9.922
## 99 39.945 0.000 4.136 4.563
## 100 69.238 0.000 4.788 5.067
## 101 95.061 0.000 5.193 5.412
## 102 94.766 0.000 5.327 5.552
## 103 74.039 0.000 4.992 5.264
## 104 86.114 0.000 5.586 5.846
## 105 103.303 0.000 5.811 6.035
## 106 79.317 0.000 5.360 5.632
## 107 81.510 0.000 5.665 5.944
## 108 84.337 0.000 5.527 5.790
## 109 74.571 0.000 5.429 5.722
## 110 6.977 0.000 2.215 3.945
## 111 8.082 0.000 2.479 4.066
## 112 6.704 0.000 2.076 3.792
## 113 6.972 0.000 2.273 4.050
## 114 6.809 0.000 2.168 3.921
## 115 8.339 0.000 2.911 4.700
## 116 NA NA 2.189 2.189
## 117 NA NA 3.310 3.310
## 118 NA NA 0.363 0.363
## 119 NA NA 5.041 5.041
## 120 NA NA 0.417 0.417
## 121 NA NA 0.078 0.078
## 122 NA NA 18.664 18.664
## 123 NA NA 5.469 5.469
## 124 NA NA 0.000 0.000
## 125 NA NA 0.000 0.000
## 126 NA NA 0.000 0.000
## 127 NA NA 0.000 0.000
## 128 -1.338 0.181 -0.379 0.072
## 129 0.241 0.810 -0.052 0.067
## 130 1.500 0.134 -0.016 0.117
## 131 -2.142 0.032 -0.286 -0.013
## 132 -0.623 0.533 -0.054 0.028
## 133 -2.321 0.020 -0.466 -0.039
## 134 0.178 0.858 -0.099 0.119
## 135 0.360 0.719 -0.111 0.161
## 136 0.443 0.658 -0.176 0.280
## 137 0.452 0.651 -0.011 0.017Outside effort
cost_oe <-'
#Measurement
hs_prep_1 =~ pre_hs_prep_1 + pre_hs_prep_2 + pre_hs_prep_3 + pre_hs_prep_4 + pre_hs_prep_5
expect =~ pre_exp_1 + pre_exp_2 + pre_exp_3
value =~ pre_val_1 + pre_val_2 + pre_val_3
a_oe_cost =~ pre_cost_oe_1 + pre_cost_oe_2 + pre_cost_oe_3 + pre_cost_oe_4
#Regressions
a_oe_cost ~ aa*hs_prep_1 + ab*pre_hours_work + ac*pre_hours_math_prep + ad*credits_more_than_15 + ae*pre_stem_int + af*expect + ag*value + ah*female + ai*urm + aj*Best_MPS
eoc_cost_oe ~ b*a_oe_cost + hs_prep_1 + pre_hours_work + pre_hours_math_prep + credits_more_than_15 + pre_stem_int + expect + value + female + urm + Best_MPS + week
#indirect effects
hs_cost:= aa*b
work_cost:= ab*b
prep_cost:= ac*b
credits_cost:= ad*b
stem_cost:= ae*b
expect_cost:= af*b
val_cost:= ag*b
female_cost:= ah*b
urm_cost:= ai*b
mps_cost:= aj*b
#Covariances
# pre_stem_int ~~ expect + value + a_te_cost
# expect ~~ value + a_te_cost
# value ~~ a_te_cost
# pre_hours_math_prep ~~ pre_hours_math_prep
'
fit <- sem(cost_oe, data=MTH_132_124_all, estimator = "MLR", cluster = "stud_id", missing = "ML.x", se = 'bootstrap')
test = parameterEstimates(fit,boot.ci.type = 'bca.simple',level=.95) # bootstrapped estimate
summary(fit, fit.measures = TRUE, standardized = TRUE)## lavaan 0.6-7 ended normally after 95 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 72
##
## Number of observations 2435
## Number of clusters [stud_id] 429
## Number of missing patterns 36
##
## Model Test User Model:
## Standard Robust
## Test Statistic 1812.427 255.233
## Degrees of freedom 208 208
## P-value (Chi-square) 0.000 0.014
## Scaling correction factor 7.101
## Yuan-Bentler correction (Mplus variant)
##
## Model Test Baseline Model:
##
## Test statistic 18313.436 2299.202
## Degrees of freedom 248 248
## P-value 0.000 0.000
## Scaling correction factor 7.965
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.911 0.977
## Tucker-Lewis Index (TLI) 0.894 0.973
##
## Robust Comparative Fit Index (CFI) 0.979
## Robust Tucker-Lewis Index (TLI) 0.976
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) NA NA
## Scaling correction factor 11.463
## for the MLR correction
## Loglikelihood unrestricted model (H1) NA NA
## Scaling correction factor 8.223
## for the MLR correction
##
## Akaike (AIC) NA NA
## Bayesian (BIC) NA NA
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.056 0.010
## 90 Percent confidence interval - lower 0.054 0.008
## 90 Percent confidence interval - upper 0.059 0.011
## P-value RMSEA <= 0.05 0.000 1.000
##
## Robust RMSEA 0.026
## 90 Percent confidence interval - lower 0.012
## 90 Percent confidence interval - upper 0.036
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.067 0.067
##
## Parameter Estimates:
##
## Standard errors Robust.cluster
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## hs_prep_1 =~
## pre_hs_prep_1 1.000 0.673 0.512
## pre_hs_prep_2 0.743 0.110 6.750 0.000 0.499 0.611
## pre_hs_prep_3 0.766 0.119 6.445 0.000 0.515 0.781
## pre_hs_prep_4 0.820 0.129 6.375 0.000 0.551 0.847
## pre_hs_prep_5 0.806 0.150 5.364 0.000 0.542 0.652
## expect =~
## pre_exp_1 1.000 0.885 0.764
## pre_exp_2 0.925 0.077 11.971 0.000 0.819 0.839
## pre_exp_3 1.108 0.074 15.046 0.000 0.981 0.818
## value =~
## pre_val_1 1.000 1.027 0.843
## pre_val_2 0.954 0.053 17.981 0.000 0.980 0.852
## pre_val_3 1.046 0.053 19.858 0.000 1.074 0.844
## a_oe_cost =~
## pre_cost_oe_1 1.000 1.035 0.810
## pre_cost_oe_2 1.062 0.066 15.978 0.000 1.099 0.865
## pre_cost_oe_3 1.054 0.069 15.269 0.000 1.091 0.836
## pre_cost_oe_4 0.985 0.065 15.136 0.000 1.020 0.817
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## a_oe_cost ~
## hs_prep_1 (aa) -0.252 0.190 -1.328 0.184 -0.164 -0.164
## pr_hrs_wr (ab) -0.001 0.048 -0.015 0.988 -0.001 -0.001
## pr_hrs_m_ (ac) -0.025 0.049 -0.519 0.604 -0.025 -0.032
## crdt___15 (ad) -0.291 0.114 -2.553 0.011 -0.281 -0.135
## pr_stm_nt (ae) -0.036 0.035 -1.016 0.310 -0.034 -0.063
## expect (af) -0.398 0.146 -2.721 0.007 -0.341 -0.341
## value (ag) 0.062 0.101 0.617 0.537 0.062 0.062
## female (ah) -0.130 0.116 -1.115 0.265 -0.125 -0.062
## urm (ai) -0.196 0.172 -1.142 0.253 -0.189 -0.051
## Best_MPS (aj) 0.001 0.014 0.058 0.954 0.001 0.004
## eoc_cost_oe ~
## a_oe_cost (b) 0.512 0.091 5.628 0.000 0.530 0.344
## hs_prep_1 -0.274 0.152 -1.797 0.072 -0.184 -0.119
## pr_hrs_wr 0.045 0.049 0.910 0.363 0.045 0.044
## pr_hrs_m_ 0.069 0.040 1.717 0.086 0.069 0.059
## crdt___15 -0.213 0.129 -1.651 0.099 -0.213 -0.067
## pr_stm_nt -0.074 0.040 -1.856 0.063 -0.074 -0.089
## expect -0.246 0.122 -2.022 0.043 -0.218 -0.141
## value 0.089 0.110 0.804 0.422 0.091 0.059
## female 0.007 0.128 0.056 0.955 0.007 0.002
## urm 0.381 0.252 1.511 0.131 0.381 0.067
## Best_MPS -0.043 0.015 -2.818 0.005 -0.043 -0.141
## week 0.029 0.011 2.707 0.007 0.029 0.060
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## hs_prep_1 ~~
## expect 0.219 0.092 2.385 0.017 0.367 0.367
## value 0.176 0.103 1.712 0.087 0.255 0.255
## expect ~~
## value 0.616 0.130 4.754 0.000 0.678 0.678
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pre_hs_prep_1 4.360 0.110 39.567 0.000 4.360 3.316
## .pre_hs_prep_2 4.935 0.072 68.572 0.000 4.935 6.037
## .pre_hs_prep_3 5.311 0.057 93.661 0.000 5.311 8.046
## .pre_hs_prep_4 5.448 0.058 93.793 0.000 5.448 8.375
## .pre_hs_prep_5 5.136 0.071 72.665 0.000 5.136 6.182
## .pre_exp_1 5.717 0.066 86.209 0.000 5.717 4.934
## .pre_exp_2 5.924 0.057 103.466 0.000 5.924 6.072
## .pre_exp_3 5.497 0.069 79.413 0.000 5.497 4.582
## .pre_val_1 5.805 0.071 81.576 0.000 5.805 4.769
## .pre_val_2 5.660 0.067 84.384 0.000 5.660 4.922
## .pre_val_3 5.576 0.075 74.584 0.000 5.576 4.386
## .pre_cost_oe_1 3.137 0.395 7.938 0.000 3.137 2.455
## .pre_cost_oe_2 3.183 0.426 7.476 0.000 3.183 2.503
## .pre_cost_oe_3 3.157 0.423 7.463 0.000 3.157 2.420
## .pre_cost_oe_4 3.126 0.399 7.839 0.000 3.126 2.504
## .eoc_cost_oe 3.767 0.452 8.332 0.000 3.767 2.444
## hs_prep_1 0.000 0.000 0.000
## expect 0.000 0.000 0.000
## value 0.000 0.000 0.000
## .a_oe_cost 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pre_hs_prep_1 1.276 0.178 7.174 0.000 1.276 0.738
## .pre_hs_prep_2 0.419 0.070 6.020 0.000 0.419 0.627
## .pre_hs_prep_3 0.170 0.034 4.958 0.000 0.170 0.390
## .pre_hs_prep_4 0.119 0.026 4.543 0.000 0.119 0.282
## .pre_hs_prep_5 0.397 0.147 2.692 0.007 0.397 0.575
## .pre_exp_1 0.559 0.160 3.483 0.000 0.559 0.416
## .pre_exp_2 0.281 0.056 5.004 0.000 0.281 0.295
## .pre_exp_3 0.477 0.091 5.237 0.000 0.477 0.331
## .pre_val_1 0.428 0.096 4.454 0.000 0.428 0.289
## .pre_val_2 0.363 0.060 6.093 0.000 0.363 0.274
## .pre_val_3 0.464 0.088 5.276 0.000 0.464 0.287
## .pre_cost_oe_1 0.562 0.110 5.105 0.000 0.562 0.344
## .pre_cost_oe_2 0.409 0.068 6.019 0.000 0.409 0.253
## .pre_cost_oe_3 0.512 0.083 6.139 0.000 0.512 0.301
## .pre_cost_oe_4 0.518 0.069 7.477 0.000 0.518 0.332
## .eoc_cost_oe 1.770 0.113 15.601 0.000 1.770 0.746
## hs_prep_1 0.452 0.121 3.736 0.000 1.000 1.000
## expect 0.784 0.158 4.956 0.000 1.000 1.000
## value 1.054 0.185 5.709 0.000 1.000 1.000
## .a_oe_cost 0.874 0.149 5.857 0.000 0.816 0.816
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## hs_cost -0.129 0.098 -1.317 0.188 -0.087 -0.056
## work_cost -0.000 0.025 -0.015 0.988 -0.000 -0.000
## prep_cost -0.013 0.026 -0.509 0.611 -0.013 -0.011
## credits_cost -0.149 0.063 -2.379 0.017 -0.149 -0.046
## stem_cost -0.018 0.017 -1.043 0.297 -0.018 -0.022
## expect_cost -0.204 0.086 -2.383 0.017 -0.180 -0.117
## val_cost 0.032 0.051 0.622 0.534 0.033 0.021
## female_cost -0.066 0.060 -1.103 0.270 -0.066 -0.021
## urm_cost -0.100 0.090 -1.116 0.264 -0.100 -0.018
## mps_cost 0.000 0.007 0.058 0.954 0.000 0.001test## lhs op rhs label est se
## 1 hs_prep_1 =~ pre_hs_prep_1 1.000 0.000
## 2 hs_prep_1 =~ pre_hs_prep_2 0.743 0.110
## 3 hs_prep_1 =~ pre_hs_prep_3 0.766 0.119
## 4 hs_prep_1 =~ pre_hs_prep_4 0.820 0.129
## 5 hs_prep_1 =~ pre_hs_prep_5 0.806 0.150
## 6 expect =~ pre_exp_1 1.000 0.000
## 7 expect =~ pre_exp_2 0.925 0.077
## 8 expect =~ pre_exp_3 1.108 0.074
## 9 value =~ pre_val_1 1.000 0.000
## 10 value =~ pre_val_2 0.954 0.053
## 11 value =~ pre_val_3 1.046 0.053
## 12 a_oe_cost =~ pre_cost_oe_1 1.000 0.000
## 13 a_oe_cost =~ pre_cost_oe_2 1.062 0.066
## 14 a_oe_cost =~ pre_cost_oe_3 1.054 0.069
## 15 a_oe_cost =~ pre_cost_oe_4 0.985 0.065
## 16 a_oe_cost ~ hs_prep_1 aa -0.252 0.190
## 17 a_oe_cost ~ pre_hours_work ab -0.001 0.048
## 18 a_oe_cost ~ pre_hours_math_prep ac -0.025 0.049
## 19 a_oe_cost ~ credits_more_than_15 ad -0.291 0.114
## 20 a_oe_cost ~ pre_stem_int ae -0.036 0.035
## 21 a_oe_cost ~ expect af -0.398 0.146
## 22 a_oe_cost ~ value ag 0.062 0.101
## 23 a_oe_cost ~ female ah -0.130 0.116
## 24 a_oe_cost ~ urm ai -0.196 0.172
## 25 a_oe_cost ~ Best_MPS aj 0.001 0.014
## 26 eoc_cost_oe ~ a_oe_cost b 0.512 0.091
## 27 eoc_cost_oe ~ hs_prep_1 -0.274 0.152
## 28 eoc_cost_oe ~ pre_hours_work 0.045 0.049
## 29 eoc_cost_oe ~ pre_hours_math_prep 0.069 0.040
## 30 eoc_cost_oe ~ credits_more_than_15 -0.213 0.129
## 31 eoc_cost_oe ~ pre_stem_int -0.074 0.040
## 32 eoc_cost_oe ~ expect -0.246 0.122
## 33 eoc_cost_oe ~ value 0.089 0.110
## 34 eoc_cost_oe ~ female 0.007 0.128
## 35 eoc_cost_oe ~ urm 0.381 0.252
## 36 eoc_cost_oe ~ Best_MPS -0.043 0.015
## 37 eoc_cost_oe ~ week 0.029 0.011
## 38 pre_hs_prep_1 ~~ pre_hs_prep_1 1.276 0.178
## 39 pre_hs_prep_2 ~~ pre_hs_prep_2 0.419 0.070
## 40 pre_hs_prep_3 ~~ pre_hs_prep_3 0.170 0.034
## 41 pre_hs_prep_4 ~~ pre_hs_prep_4 0.119 0.026
## 42 pre_hs_prep_5 ~~ pre_hs_prep_5 0.397 0.147
## 43 pre_exp_1 ~~ pre_exp_1 0.559 0.160
## 44 pre_exp_2 ~~ pre_exp_2 0.281 0.056
## 45 pre_exp_3 ~~ pre_exp_3 0.477 0.091
## 46 pre_val_1 ~~ pre_val_1 0.428 0.096
## 47 pre_val_2 ~~ pre_val_2 0.363 0.060
## 48 pre_val_3 ~~ pre_val_3 0.464 0.088
## 49 pre_cost_oe_1 ~~ pre_cost_oe_1 0.562 0.110
## 50 pre_cost_oe_2 ~~ pre_cost_oe_2 0.409 0.068
## 51 pre_cost_oe_3 ~~ pre_cost_oe_3 0.512 0.083
## 52 pre_cost_oe_4 ~~ pre_cost_oe_4 0.518 0.069
## 53 eoc_cost_oe ~~ eoc_cost_oe 1.770 0.113
## 54 hs_prep_1 ~~ hs_prep_1 0.452 0.121
## 55 expect ~~ expect 0.784 0.158
## 56 value ~~ value 1.054 0.185
## 57 a_oe_cost ~~ a_oe_cost 0.874 0.149
## 58 hs_prep_1 ~~ expect 0.219 0.092
## 59 hs_prep_1 ~~ value 0.176 0.103
## 60 expect ~~ value 0.616 0.130
## 61 pre_hours_work ~~ pre_hours_work 2.327 0.000
## 62 pre_hours_work ~~ pre_hours_math_prep 0.159 0.000
## 63 pre_hours_work ~~ credits_more_than_15 -0.031 0.000
## 64 pre_hours_work ~~ pre_stem_int 0.322 0.000
## 65 pre_hours_work ~~ female 0.089 0.000
## 66 pre_hours_work ~~ urm 0.087 0.000
## 67 pre_hours_work ~~ Best_MPS -0.602 0.000
## 68 pre_hours_work ~~ week -0.012 0.000
## 69 pre_hours_math_prep ~~ pre_hours_math_prep 1.744 0.000
## 70 pre_hours_math_prep ~~ credits_more_than_15 -0.033 0.000
## 71 pre_hours_math_prep ~~ pre_stem_int 0.054 0.000
## 72 pre_hours_math_prep ~~ female -0.053 0.000
## 73 pre_hours_math_prep ~~ urm -0.003 0.000
## 74 pre_hours_math_prep ~~ Best_MPS 0.459 0.000
## 75 pre_hours_math_prep ~~ week -0.082 0.000
## 76 credits_more_than_15 ~~ credits_more_than_15 0.231 0.000
## 77 credits_more_than_15 ~~ pre_stem_int -0.067 0.000
## 78 credits_more_than_15 ~~ female 0.007 0.000
## 79 credits_more_than_15 ~~ urm 0.007 0.000
## 80 credits_more_than_15 ~~ Best_MPS -0.085 0.000
## 81 credits_more_than_15 ~~ week 0.004 0.000
## 82 pre_stem_int ~~ pre_stem_int 3.383 0.000
## 83 pre_stem_int ~~ female 0.006 0.000
## 84 pre_stem_int ~~ urm 0.031 0.000
## 85 pre_stem_int ~~ Best_MPS 0.254 0.000
## 86 pre_stem_int ~~ week -0.122 0.000
## 87 female ~~ female 0.243 0.000
## 88 female ~~ urm 0.012 0.000
## 89 female ~~ Best_MPS -0.228 0.000
## 90 female ~~ week 0.077 0.000
## 91 urm ~~ urm 0.072 0.000
## 92 urm ~~ Best_MPS -0.369 0.000
## 93 urm ~~ week -0.008 0.000
## 94 Best_MPS ~~ Best_MPS 25.341 0.000
## 95 Best_MPS ~~ week -0.328 0.000
## 96 week ~~ week 9.922 0.000
## 97 pre_hs_prep_1 ~1 4.360 0.110
## 98 pre_hs_prep_2 ~1 4.935 0.072
## 99 pre_hs_prep_3 ~1 5.311 0.057
## 100 pre_hs_prep_4 ~1 5.448 0.058
## 101 pre_hs_prep_5 ~1 5.136 0.071
## 102 pre_exp_1 ~1 5.717 0.066
## 103 pre_exp_2 ~1 5.924 0.057
## 104 pre_exp_3 ~1 5.497 0.069
## 105 pre_val_1 ~1 5.805 0.071
## 106 pre_val_2 ~1 5.660 0.067
## 107 pre_val_3 ~1 5.576 0.075
## 108 pre_cost_oe_1 ~1 3.137 0.395
## 109 pre_cost_oe_2 ~1 3.183 0.426
## 110 pre_cost_oe_3 ~1 3.157 0.423
## 111 pre_cost_oe_4 ~1 3.126 0.399
## 112 eoc_cost_oe ~1 3.767 0.452
## 113 pre_hours_work ~1 2.189 0.000
## 114 pre_hours_math_prep ~1 3.310 0.000
## 115 credits_more_than_15 ~1 0.363 0.000
## 116 pre_stem_int ~1 5.041 0.000
## 117 female ~1 0.417 0.000
## 118 urm ~1 0.078 0.000
## 119 Best_MPS ~1 18.701 0.000
## 120 week ~1 5.469 0.000
## 121 hs_prep_1 ~1 0.000 0.000
## 122 expect ~1 0.000 0.000
## 123 value ~1 0.000 0.000
## 124 a_oe_cost ~1 0.000 0.000
## 125 hs_cost := aa*b hs_cost -0.129 0.098
## 126 work_cost := ab*b work_cost 0.000 0.025
## 127 prep_cost := ac*b prep_cost -0.013 0.026
## 128 credits_cost := ad*b credits_cost -0.149 0.063
## 129 stem_cost := ae*b stem_cost -0.018 0.017
## 130 expect_cost := af*b expect_cost -0.204 0.086
## 131 val_cost := ag*b val_cost 0.032 0.051
## 132 female_cost := ah*b female_cost -0.066 0.060
## 133 urm_cost := ai*b urm_cost -0.100 0.090
## 134 mps_cost := aj*b mps_cost 0.000 0.007
## z pvalue ci.lower ci.upper
## 1 NA NA 1.000 1.000
## 2 6.750 0.000 0.527 0.958
## 3 6.445 0.000 0.533 0.999
## 4 6.375 0.000 0.568 1.072
## 5 5.364 0.000 0.511 1.100
## 6 NA NA 1.000 1.000
## 7 11.971 0.000 0.774 1.077
## 8 15.046 0.000 0.964 1.253
## 9 NA NA 1.000 1.000
## 10 17.981 0.000 0.850 1.058
## 11 19.858 0.000 0.943 1.149
## 12 NA NA 1.000 1.000
## 13 15.978 0.000 0.932 1.193
## 14 15.269 0.000 0.919 1.189
## 15 15.136 0.000 0.858 1.113
## 16 -1.328 0.184 -0.625 0.120
## 17 -0.015 0.988 -0.095 0.093
## 18 -0.519 0.604 -0.122 0.071
## 19 -2.553 0.011 -0.514 -0.068
## 20 -1.016 0.310 -0.104 0.033
## 21 -2.721 0.007 -0.685 -0.111
## 22 0.617 0.537 -0.135 0.259
## 23 -1.115 0.265 -0.358 0.098
## 24 -1.142 0.253 -0.533 0.140
## 25 0.058 0.954 -0.026 0.028
## 26 5.628 0.000 0.333 0.690
## 27 -1.797 0.072 -0.572 0.025
## 28 0.910 0.363 -0.051 0.140
## 29 1.717 0.086 -0.010 0.147
## 30 -1.651 0.099 -0.466 0.040
## 31 -1.856 0.063 -0.153 0.004
## 32 -2.022 0.043 -0.484 -0.008
## 33 0.804 0.422 -0.128 0.305
## 34 0.056 0.955 -0.243 0.258
## 35 1.511 0.131 -0.113 0.876
## 36 -2.818 0.005 -0.073 -0.013
## 37 2.707 0.007 0.008 0.051
## 38 7.174 0.000 0.928 1.625
## 39 6.020 0.000 0.283 0.555
## 40 4.958 0.000 0.103 0.237
## 41 4.543 0.000 0.068 0.171
## 42 2.692 0.007 0.108 0.685
## 43 3.483 0.000 0.244 0.873
## 44 5.004 0.000 0.171 0.391
## 45 5.237 0.000 0.298 0.655
## 46 4.454 0.000 0.240 0.616
## 47 6.093 0.000 0.246 0.479
## 48 5.276 0.000 0.291 0.636
## 49 5.105 0.000 0.346 0.777
## 50 6.019 0.000 0.275 0.542
## 51 6.139 0.000 0.349 0.676
## 52 7.477 0.000 0.382 0.654
## 53 15.601 0.000 1.548 1.993
## 54 3.736 0.000 0.215 0.690
## 55 4.956 0.000 0.474 1.094
## 56 5.709 0.000 0.692 1.416
## 57 5.857 0.000 0.581 1.166
## 58 2.385 0.017 0.039 0.398
## 59 1.712 0.087 -0.025 0.377
## 60 4.754 0.000 0.362 0.870
## 61 NA NA 2.327 2.327
## 62 NA NA 0.159 0.159
## 63 NA NA -0.031 -0.031
## 64 NA NA 0.322 0.322
## 65 NA NA 0.089 0.089
## 66 NA NA 0.087 0.087
## 67 NA NA -0.602 -0.602
## 68 NA NA -0.012 -0.012
## 69 NA NA 1.744 1.744
## 70 NA NA -0.033 -0.033
## 71 NA NA 0.054 0.054
## 72 NA NA -0.053 -0.053
## 73 NA NA -0.003 -0.003
## 74 NA NA 0.459 0.459
## 75 NA NA -0.082 -0.082
## 76 NA NA 0.231 0.231
## 77 NA NA -0.067 -0.067
## 78 NA NA 0.007 0.007
## 79 NA NA 0.007 0.007
## 80 NA NA -0.085 -0.085
## 81 NA NA 0.004 0.004
## 82 NA NA 3.383 3.383
## 83 NA NA 0.006 0.006
## 84 NA NA 0.031 0.031
## 85 NA NA 0.254 0.254
## 86 NA NA -0.122 -0.122
## 87 NA NA 0.243 0.243
## 88 NA NA 0.012 0.012
## 89 NA NA -0.228 -0.228
## 90 NA NA 0.077 0.077
## 91 NA NA 0.072 0.072
## 92 NA NA -0.369 -0.369
## 93 NA NA -0.008 -0.008
## 94 NA NA 25.341 25.341
## 95 NA NA -0.328 -0.328
## 96 NA NA 9.922 9.922
## 97 39.567 0.000 4.144 4.576
## 98 68.572 0.000 4.794 5.076
## 99 93.661 0.000 5.200 5.422
## 100 93.793 0.000 5.334 5.561
## 101 72.665 0.000 4.998 5.275
## 102 86.209 0.000 5.587 5.847
## 103 103.466 0.000 5.812 6.036
## 104 79.413 0.000 5.361 5.633
## 105 81.576 0.000 5.666 5.945
## 106 84.384 0.000 5.528 5.791
## 107 74.584 0.000 5.430 5.723
## 108 7.938 0.000 2.362 3.912
## 109 7.476 0.000 2.349 4.017
## 110 7.463 0.000 2.328 3.986
## 111 7.839 0.000 2.344 3.907
## 112 8.332 0.000 2.881 4.653
## 113 NA NA 2.189 2.189
## 114 NA NA 3.310 3.310
## 115 NA NA 0.363 0.363
## 116 NA NA 5.041 5.041
## 117 NA NA 0.417 0.417
## 118 NA NA 0.078 0.078
## 119 NA NA 18.701 18.701
## 120 NA NA 5.469 5.469
## 121 NA NA 0.000 0.000
## 122 NA NA 0.000 0.000
## 123 NA NA 0.000 0.000
## 124 NA NA 0.000 0.000
## 125 -1.317 0.188 -0.321 0.063
## 126 -0.015 0.988 -0.049 0.048
## 127 -0.509 0.611 -0.063 0.037
## 128 -2.379 0.017 -0.271 -0.026
## 129 -1.043 0.297 -0.052 0.016
## 130 -2.383 0.017 -0.371 -0.036
## 131 0.622 0.534 -0.068 0.132
## 132 -1.103 0.270 -0.184 0.052
## 133 -1.116 0.264 -0.276 0.076
## 134 0.058 0.954 -0.014 0.014Loss of Valued Alternatives
cost_lv <-'
#Measurement
hs_prep_1 =~ pre_hs_prep_1 + pre_hs_prep_2 + pre_hs_prep_3 + pre_hs_prep_4 + pre_hs_prep_5
expect =~ pre_exp_1 + pre_exp_2 + pre_exp_3
value =~ pre_val_1 + pre_val_2 + pre_val_3
a_lv_cost =~ pre_cost_lv_1 + pre_cost_lv_2 + pre_cost_lv_3 + pre_cost_lv_4
#Regressions
a_lv_cost ~ aa*hs_prep_1 + ab*pre_hours_work + ac*pre_hours_math_prep + ad*credits_more_than_15 + ae*pre_stem_int + af*expect + ag*value + ah*female + ai*urm + aj*Best_MPS
eoc_cost_lv ~ b*a_lv_cost + hs_prep_1 + pre_hours_work + pre_hours_math_prep + credits_more_than_15 + pre_stem_int + expect + value + female + urm + Best_MPS + week
#indirect effects
hs_cost:= aa*b
work_cost:= ab*b
prep_cost:= ac*b
credits_cost:= ad*b
stem_cost:= ae*b
expect_cost:= af*b
val_cost:= ag*b
female_cost:= ah*b
urm_cost:= ai*b
mps_cost:= aj*b
#Covariances
#expect ~~ value
#pre_hours_math_prep ~~ pre_hours_math_prep
'
fit <- sem(cost_lv, data=MTH_132_124_all, estimator = "MLR", cluster = "stud_id", missing = "ML.x", se = 'bootstrap')
test = parameterEstimates(fit,boot.ci.type = 'bca.simple',level=.95) # bootstrapped estimate
summary(fit, fit.measures = TRUE, standardized = TRUE)## lavaan 0.6-7 ended normally after 91 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 72
##
## Number of observations 2435
## Number of clusters [stud_id] 429
## Number of missing patterns 39
##
## Model Test User Model:
## Standard Robust
## Test Statistic 1952.162 276.246
## Degrees of freedom 208 208
## P-value (Chi-square) 0.000 0.001
## Scaling correction factor 7.067
## Yuan-Bentler correction (Mplus variant)
##
## Model Test Baseline Model:
##
## Test statistic 17246.845 2182.777
## Degrees of freedom 248 248
## P-value 0.000 0.000
## Scaling correction factor 7.901
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.897 0.965
## Tucker-Lewis Index (TLI) 0.878 0.958
##
## Robust Comparative Fit Index (CFI) 0.968
## Robust Tucker-Lewis Index (TLI) 0.962
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) NA NA
## Scaling correction factor 11.196
## for the MLR correction
## Loglikelihood unrestricted model (H1) NA NA
## Scaling correction factor 8.128
## for the MLR correction
##
## Akaike (AIC) NA NA
## Bayesian (BIC) NA NA
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.059 0.012
## 90 Percent confidence interval - lower 0.056 0.010
## 90 Percent confidence interval - upper 0.061 0.013
## P-value RMSEA <= 0.05 0.000 1.000
##
## Robust RMSEA 0.031
## 90 Percent confidence interval - lower 0.020
## 90 Percent confidence interval - upper 0.040
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.064 0.064
##
## Parameter Estimates:
##
## Standard errors Robust.cluster
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## hs_prep_1 =~
## pre_hs_prep_1 1.000 0.679 0.517
## pre_hs_prep_2 0.736 0.109 6.742 0.000 0.500 0.612
## pre_hs_prep_3 0.760 0.119 6.374 0.000 0.516 0.783
## pre_hs_prep_4 0.803 0.127 6.327 0.000 0.545 0.840
## pre_hs_prep_5 0.796 0.150 5.318 0.000 0.540 0.651
## expect =~
## pre_exp_1 1.000 0.886 0.764
## pre_exp_2 0.912 0.077 11.774 0.000 0.808 0.828
## pre_exp_3 1.125 0.078 14.349 0.000 0.997 0.830
## value =~
## pre_val_1 1.000 1.027 0.844
## pre_val_2 0.953 0.053 18.028 0.000 0.978 0.851
## pre_val_3 1.047 0.053 19.817 0.000 1.075 0.845
## a_lv_cost =~
## pre_cost_lv_1 1.000 1.005 0.767
## pre_cost_lv_2 1.125 0.068 16.458 0.000 1.130 0.838
## pre_cost_lv_3 1.204 0.085 14.213 0.000 1.210 0.879
## pre_cost_lv_4 0.921 0.093 9.928 0.000 0.925 0.613
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## a_lv_cost ~
## hs_prep_1 (aa) -0.209 0.193 -1.086 0.278 -0.141 -0.141
## pr_hrs_wr (ab) -0.022 0.050 -0.443 0.657 -0.022 -0.033
## pr_hrs_m_ (ac) 0.047 0.051 0.925 0.355 0.047 0.062
## crdt___15 (ad) -0.397 0.110 -3.613 0.000 -0.395 -0.190
## pr_stm_nt (ae) -0.003 0.036 -0.087 0.931 -0.003 -0.006
## expect (af) -0.316 0.150 -2.109 0.035 -0.278 -0.278
## value (ag) -0.041 0.096 -0.428 0.669 -0.042 -0.042
## female (ah) -0.050 0.116 -0.429 0.668 -0.049 -0.024
## urm (ai) 0.218 0.195 1.122 0.262 0.217 0.058
## Best_MPS (aj) 0.024 0.012 1.964 0.050 0.024 0.119
## eoc_cost_lv ~
## a_lv_cost (b) 0.566 0.100 5.654 0.000 0.569 0.363
## hs_prep_1 -0.351 0.142 -2.477 0.013 -0.238 -0.152
## pr_hrs_wr 0.064 0.046 1.388 0.165 0.064 0.062
## pr_hrs_m_ 0.087 0.044 2.004 0.045 0.087 0.074
## crdt___15 -0.239 0.130 -1.832 0.067 -0.239 -0.073
## pr_stm_nt -0.061 0.037 -1.672 0.095 -0.061 -0.072
## expect -0.356 0.152 -2.346 0.019 -0.315 -0.201
## value 0.177 0.120 1.469 0.142 0.182 0.116
## female -0.082 0.127 -0.649 0.516 -0.082 -0.026
## urm 0.138 0.246 0.562 0.574 0.138 0.024
## Best_MPS -0.048 0.014 -3.358 0.001 -0.048 -0.156
## week 0.025 0.011 2.262 0.024 0.025 0.050
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## hs_prep_1 ~~
## expect 0.216 0.094 2.300 0.021 0.359 0.359
## value 0.178 0.104 1.717 0.086 0.255 0.255
## expect ~~
## value 0.616 0.130 4.732 0.000 0.677 0.677
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pre_hs_prep_1 4.358 0.110 39.735 0.000 4.358 3.317
## .pre_hs_prep_2 4.934 0.072 68.814 0.000 4.934 6.041
## .pre_hs_prep_3 5.309 0.056 93.996 0.000 5.309 8.055
## .pre_hs_prep_4 5.446 0.058 94.021 0.000 5.446 8.391
## .pre_hs_prep_5 5.135 0.070 73.048 0.000 5.135 6.188
## .pre_exp_1 5.716 0.066 86.099 0.000 5.716 4.933
## .pre_exp_2 5.923 0.057 103.296 0.000 5.923 6.071
## .pre_exp_3 5.496 0.069 79.316 0.000 5.496 4.578
## .pre_val_1 5.805 0.071 81.473 0.000 5.805 4.768
## .pre_val_2 5.659 0.067 84.306 0.000 5.659 4.921
## .pre_val_3 5.576 0.075 74.539 0.000 5.576 4.385
## .pre_cost_lv_1 2.450 0.377 6.496 0.000 2.450 1.870
## .pre_cost_lv_2 2.561 0.422 6.075 0.000 2.561 1.899
## .pre_cost_lv_3 2.541 0.451 5.639 0.000 2.541 1.845
## .pre_cost_lv_4 3.372 0.351 9.599 0.000 3.372 2.235
## .eoc_cost_lv 3.387 0.436 7.761 0.000 3.387 2.164
## hs_prep_1 0.000 0.000 0.000
## expect 0.000 0.000 0.000
## value 0.000 0.000 0.000
## .a_lv_cost 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pre_hs_prep_1 1.265 0.177 7.131 0.000 1.265 0.733
## .pre_hs_prep_2 0.417 0.068 6.166 0.000 0.417 0.625
## .pre_hs_prep_3 0.168 0.033 5.033 0.000 0.168 0.387
## .pre_hs_prep_4 0.124 0.028 4.374 0.000 0.124 0.294
## .pre_hs_prep_5 0.396 0.149 2.667 0.008 0.396 0.576
## .pre_exp_1 0.558 0.159 3.509 0.000 0.558 0.416
## .pre_exp_2 0.299 0.060 5.000 0.000 0.299 0.314
## .pre_exp_3 0.448 0.088 5.116 0.000 0.448 0.311
## .pre_val_1 0.427 0.095 4.508 0.000 0.427 0.288
## .pre_val_2 0.365 0.060 6.092 0.000 0.365 0.276
## .pre_val_3 0.461 0.086 5.376 0.000 0.461 0.285
## .pre_cost_lv_1 0.707 0.094 7.504 0.000 0.707 0.412
## .pre_cost_lv_2 0.542 0.089 6.105 0.000 0.542 0.298
## .pre_cost_lv_3 0.432 0.070 6.186 0.000 0.432 0.228
## .pre_cost_lv_4 1.420 0.139 10.222 0.000 1.420 0.624
## .eoc_cost_lv 1.722 0.115 15.033 0.000 1.722 0.703
## hs_prep_1 0.461 0.124 3.727 0.000 1.000 1.000
## expect 0.784 0.159 4.928 0.000 1.000 1.000
## value 1.055 0.185 5.700 0.000 1.000 1.000
## .a_lv_cost 0.804 0.140 5.744 0.000 0.796 0.796
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## hs_cost -0.118 0.106 -1.116 0.265 -0.080 -0.051
## work_cost -0.012 0.027 -0.457 0.648 -0.012 -0.012
## prep_cost 0.027 0.030 0.905 0.366 0.027 0.023
## credits_cost -0.225 0.073 -3.092 0.002 -0.225 -0.069
## stem_cost -0.002 0.021 -0.087 0.931 -0.002 -0.002
## expect_cost -0.179 0.096 -1.859 0.063 -0.158 -0.101
## val_cost -0.023 0.055 -0.426 0.670 -0.024 -0.015
## female_cost -0.028 0.066 -0.426 0.670 -0.028 -0.009
## urm_cost 0.124 0.110 1.125 0.260 0.124 0.021
## mps_cost 0.013 0.007 1.800 0.072 0.013 0.043test## lhs op rhs label est se
## 1 hs_prep_1 =~ pre_hs_prep_1 1.000 0.000
## 2 hs_prep_1 =~ pre_hs_prep_2 0.736 0.109
## 3 hs_prep_1 =~ pre_hs_prep_3 0.760 0.119
## 4 hs_prep_1 =~ pre_hs_prep_4 0.803 0.127
## 5 hs_prep_1 =~ pre_hs_prep_5 0.796 0.150
## 6 expect =~ pre_exp_1 1.000 0.000
## 7 expect =~ pre_exp_2 0.912 0.077
## 8 expect =~ pre_exp_3 1.125 0.078
## 9 value =~ pre_val_1 1.000 0.000
## 10 value =~ pre_val_2 0.953 0.053
## 11 value =~ pre_val_3 1.047 0.053
## 12 a_lv_cost =~ pre_cost_lv_1 1.000 0.000
## 13 a_lv_cost =~ pre_cost_lv_2 1.125 0.068
## 14 a_lv_cost =~ pre_cost_lv_3 1.204 0.085
## 15 a_lv_cost =~ pre_cost_lv_4 0.921 0.093
## 16 a_lv_cost ~ hs_prep_1 aa -0.209 0.193
## 17 a_lv_cost ~ pre_hours_work ab -0.022 0.050
## 18 a_lv_cost ~ pre_hours_math_prep ac 0.047 0.051
## 19 a_lv_cost ~ credits_more_than_15 ad -0.397 0.110
## 20 a_lv_cost ~ pre_stem_int ae -0.003 0.036
## 21 a_lv_cost ~ expect af -0.316 0.150
## 22 a_lv_cost ~ value ag -0.041 0.096
## 23 a_lv_cost ~ female ah -0.050 0.116
## 24 a_lv_cost ~ urm ai 0.218 0.195
## 25 a_lv_cost ~ Best_MPS aj 0.024 0.012
## 26 eoc_cost_lv ~ a_lv_cost b 0.566 0.100
## 27 eoc_cost_lv ~ hs_prep_1 -0.351 0.142
## 28 eoc_cost_lv ~ pre_hours_work 0.064 0.046
## 29 eoc_cost_lv ~ pre_hours_math_prep 0.087 0.044
## 30 eoc_cost_lv ~ credits_more_than_15 -0.239 0.130
## 31 eoc_cost_lv ~ pre_stem_int -0.061 0.037
## 32 eoc_cost_lv ~ expect -0.356 0.152
## 33 eoc_cost_lv ~ value 0.177 0.120
## 34 eoc_cost_lv ~ female -0.082 0.127
## 35 eoc_cost_lv ~ urm 0.138 0.246
## 36 eoc_cost_lv ~ Best_MPS -0.048 0.014
## 37 eoc_cost_lv ~ week 0.025 0.011
## 38 pre_hs_prep_1 ~~ pre_hs_prep_1 1.265 0.177
## 39 pre_hs_prep_2 ~~ pre_hs_prep_2 0.417 0.068
## 40 pre_hs_prep_3 ~~ pre_hs_prep_3 0.168 0.033
## 41 pre_hs_prep_4 ~~ pre_hs_prep_4 0.124 0.028
## 42 pre_hs_prep_5 ~~ pre_hs_prep_5 0.396 0.149
## 43 pre_exp_1 ~~ pre_exp_1 0.558 0.159
## 44 pre_exp_2 ~~ pre_exp_2 0.299 0.060
## 45 pre_exp_3 ~~ pre_exp_3 0.448 0.088
## 46 pre_val_1 ~~ pre_val_1 0.427 0.095
## 47 pre_val_2 ~~ pre_val_2 0.365 0.060
## 48 pre_val_3 ~~ pre_val_3 0.461 0.086
## 49 pre_cost_lv_1 ~~ pre_cost_lv_1 0.707 0.094
## 50 pre_cost_lv_2 ~~ pre_cost_lv_2 0.542 0.089
## 51 pre_cost_lv_3 ~~ pre_cost_lv_3 0.432 0.070
## 52 pre_cost_lv_4 ~~ pre_cost_lv_4 1.420 0.139
## 53 eoc_cost_lv ~~ eoc_cost_lv 1.722 0.115
## 54 hs_prep_1 ~~ hs_prep_1 0.461 0.124
## 55 expect ~~ expect 0.784 0.159
## 56 value ~~ value 1.055 0.185
## 57 a_lv_cost ~~ a_lv_cost 0.804 0.140
## 58 hs_prep_1 ~~ expect 0.216 0.094
## 59 hs_prep_1 ~~ value 0.178 0.104
## 60 expect ~~ value 0.616 0.130
## 61 pre_hours_work ~~ pre_hours_work 2.327 0.000
## 62 pre_hours_work ~~ pre_hours_math_prep 0.159 0.000
## 63 pre_hours_work ~~ credits_more_than_15 -0.031 0.000
## 64 pre_hours_work ~~ pre_stem_int 0.335 0.000
## 65 pre_hours_work ~~ female 0.089 0.000
## 66 pre_hours_work ~~ urm 0.087 0.000
## 67 pre_hours_work ~~ Best_MPS -0.645 0.000
## 68 pre_hours_work ~~ week -0.012 0.000
## 69 pre_hours_math_prep ~~ pre_hours_math_prep 1.744 0.000
## 70 pre_hours_math_prep ~~ credits_more_than_15 -0.033 0.000
## 71 pre_hours_math_prep ~~ pre_stem_int 0.056 0.000
## 72 pre_hours_math_prep ~~ female -0.053 0.000
## 73 pre_hours_math_prep ~~ urm -0.003 0.000
## 74 pre_hours_math_prep ~~ Best_MPS 0.434 0.000
## 75 pre_hours_math_prep ~~ week -0.082 0.000
## 76 credits_more_than_15 ~~ credits_more_than_15 0.231 0.000
## 77 credits_more_than_15 ~~ pre_stem_int -0.069 0.000
## 78 credits_more_than_15 ~~ female 0.007 0.000
## 79 credits_more_than_15 ~~ urm 0.007 0.000
## 80 credits_more_than_15 ~~ Best_MPS -0.071 0.000
## 81 credits_more_than_15 ~~ week 0.004 0.000
## 82 pre_stem_int ~~ pre_stem_int 3.389 0.000
## 83 pre_stem_int ~~ female 0.008 0.000
## 84 pre_stem_int ~~ urm 0.030 0.000
## 85 pre_stem_int ~~ Best_MPS 0.277 0.000
## 86 pre_stem_int ~~ week -0.121 0.000
## 87 female ~~ female 0.243 0.000
## 88 female ~~ urm 0.012 0.000
## 89 female ~~ Best_MPS -0.221 0.000
## 90 female ~~ week 0.077 0.000
## 91 urm ~~ urm 0.072 0.000
## 92 urm ~~ Best_MPS -0.377 0.000
## 93 urm ~~ week -0.008 0.000
## 94 Best_MPS ~~ Best_MPS 25.367 0.000
## 95 Best_MPS ~~ week -0.320 0.000
## 96 week ~~ week 9.922 0.000
## 97 pre_hs_prep_1 ~1 4.358 0.110
## 98 pre_hs_prep_2 ~1 4.934 0.072
## 99 pre_hs_prep_3 ~1 5.309 0.056
## 100 pre_hs_prep_4 ~1 5.446 0.058
## 101 pre_hs_prep_5 ~1 5.135 0.070
## 102 pre_exp_1 ~1 5.716 0.066
## 103 pre_exp_2 ~1 5.923 0.057
## 104 pre_exp_3 ~1 5.496 0.069
## 105 pre_val_1 ~1 5.805 0.071
## 106 pre_val_2 ~1 5.659 0.067
## 107 pre_val_3 ~1 5.576 0.075
## 108 pre_cost_lv_1 ~1 2.450 0.377
## 109 pre_cost_lv_2 ~1 2.561 0.422
## 110 pre_cost_lv_3 ~1 2.541 0.451
## 111 pre_cost_lv_4 ~1 3.372 0.351
## 112 eoc_cost_lv ~1 3.387 0.436
## 113 pre_hours_work ~1 2.189 0.000
## 114 pre_hours_math_prep ~1 3.310 0.000
## 115 credits_more_than_15 ~1 0.363 0.000
## 116 pre_stem_int ~1 5.045 0.000
## 117 female ~1 0.417 0.000
## 118 urm ~1 0.078 0.000
## 119 Best_MPS ~1 18.655 0.000
## 120 week ~1 5.469 0.000
## 121 hs_prep_1 ~1 0.000 0.000
## 122 expect ~1 0.000 0.000
## 123 value ~1 0.000 0.000
## 124 a_lv_cost ~1 0.000 0.000
## 125 hs_cost := aa*b hs_cost -0.118 0.106
## 126 work_cost := ab*b work_cost -0.012 0.027
## 127 prep_cost := ac*b prep_cost 0.027 0.030
## 128 credits_cost := ad*b credits_cost -0.225 0.073
## 129 stem_cost := ae*b stem_cost -0.002 0.021
## 130 expect_cost := af*b expect_cost -0.179 0.096
## 131 val_cost := ag*b val_cost -0.023 0.055
## 132 female_cost := ah*b female_cost -0.028 0.066
## 133 urm_cost := ai*b urm_cost 0.124 0.110
## 134 mps_cost := aj*b mps_cost 0.013 0.007
## z pvalue ci.lower ci.upper
## 1 NA NA 1.000 1.000
## 2 6.742 0.000 0.522 0.950
## 3 6.374 0.000 0.526 0.994
## 4 6.327 0.000 0.554 1.052
## 5 5.318 0.000 0.502 1.089
## 6 NA NA 1.000 1.000
## 7 11.774 0.000 0.760 1.064
## 8 14.349 0.000 0.972 1.279
## 9 NA NA 1.000 1.000
## 10 18.028 0.000 0.849 1.056
## 11 19.817 0.000 0.943 1.150
## 12 NA NA 1.000 1.000
## 13 16.458 0.000 0.991 1.259
## 14 14.213 0.000 1.038 1.370
## 15 9.928 0.000 0.739 1.102
## 16 -1.086 0.278 -0.587 0.169
## 17 -0.443 0.657 -0.119 0.075
## 18 0.925 0.355 -0.053 0.147
## 19 -3.613 0.000 -0.613 -0.182
## 20 -0.087 0.931 -0.074 0.068
## 21 -2.109 0.035 -0.610 -0.022
## 22 -0.428 0.669 -0.229 0.147
## 23 -0.429 0.668 -0.277 0.177
## 24 1.122 0.262 -0.163 0.600
## 25 1.964 0.050 0.000 0.047
## 26 5.654 0.000 0.370 0.762
## 27 -2.477 0.013 -0.628 -0.073
## 28 1.388 0.165 -0.026 0.154
## 29 2.004 0.045 0.002 0.173
## 30 -1.832 0.067 -0.494 0.017
## 31 -1.672 0.095 -0.133 0.011
## 32 -2.346 0.019 -0.653 -0.058
## 33 1.469 0.142 -0.059 0.413
## 34 -0.649 0.516 -0.331 0.166
## 35 0.562 0.574 -0.344 0.621
## 36 -3.358 0.001 -0.077 -0.020
## 37 2.262 0.024 0.003 0.046
## 38 7.131 0.000 0.918 1.613
## 39 6.166 0.000 0.285 0.550
## 40 5.033 0.000 0.103 0.233
## 41 4.374 0.000 0.068 0.179
## 42 2.667 0.008 0.105 0.688
## 43 3.509 0.000 0.246 0.870
## 44 5.000 0.000 0.182 0.417
## 45 5.116 0.000 0.276 0.620
## 46 4.508 0.000 0.242 0.613
## 47 6.092 0.000 0.248 0.483
## 48 5.376 0.000 0.293 0.630
## 49 7.504 0.000 0.522 0.892
## 50 6.105 0.000 0.368 0.716
## 51 6.186 0.000 0.295 0.569
## 52 10.222 0.000 1.148 1.692
## 53 15.033 0.000 1.497 1.946
## 54 3.727 0.000 0.219 0.704
## 55 4.928 0.000 0.472 1.096
## 56 5.700 0.000 0.692 1.417
## 57 5.744 0.000 0.530 1.078
## 58 2.300 0.021 0.032 0.400
## 59 1.717 0.086 -0.025 0.381
## 60 4.732 0.000 0.361 0.871
## 61 NA NA 2.327 2.327
## 62 NA NA 0.159 0.159
## 63 NA NA -0.031 -0.031
## 64 NA NA 0.335 0.335
## 65 NA NA 0.089 0.089
## 66 NA NA 0.087 0.087
## 67 NA NA -0.645 -0.645
## 68 NA NA -0.012 -0.012
## 69 NA NA 1.744 1.744
## 70 NA NA -0.033 -0.033
## 71 NA NA 0.056 0.056
## 72 NA NA -0.053 -0.053
## 73 NA NA -0.003 -0.003
## 74 NA NA 0.434 0.434
## 75 NA NA -0.082 -0.082
## 76 NA NA 0.231 0.231
## 77 NA NA -0.069 -0.069
## 78 NA NA 0.007 0.007
## 79 NA NA 0.007 0.007
## 80 NA NA -0.071 -0.071
## 81 NA NA 0.004 0.004
## 82 NA NA 3.389 3.389
## 83 NA NA 0.008 0.008
## 84 NA NA 0.030 0.030
## 85 NA NA 0.277 0.277
## 86 NA NA -0.121 -0.121
## 87 NA NA 0.243 0.243
## 88 NA NA 0.012 0.012
## 89 NA NA -0.221 -0.221
## 90 NA NA 0.077 0.077
## 91 NA NA 0.072 0.072
## 92 NA NA -0.377 -0.377
## 93 NA NA -0.008 -0.008
## 94 NA NA 25.367 25.367
## 95 NA NA -0.320 -0.320
## 96 NA NA 9.922 9.922
## 97 39.735 0.000 4.143 4.573
## 98 68.814 0.000 4.794 5.075
## 99 93.996 0.000 5.199 5.420
## 100 94.021 0.000 5.333 5.560
## 101 73.048 0.000 4.997 5.273
## 102 86.099 0.000 5.586 5.846
## 103 103.296 0.000 5.811 6.036
## 104 79.316 0.000 5.360 5.632
## 105 81.473 0.000 5.665 5.944
## 106 84.306 0.000 5.527 5.791
## 107 74.539 0.000 5.429 5.723
## 108 6.496 0.000 1.711 3.189
## 109 6.075 0.000 1.735 3.388
## 110 5.639 0.000 1.658 3.424
## 111 9.599 0.000 2.683 4.060
## 112 7.761 0.000 2.532 4.242
## 113 NA NA 2.189 2.189
## 114 NA NA 3.310 3.310
## 115 NA NA 0.363 0.363
## 116 NA NA 5.045 5.045
## 117 NA NA 0.417 0.417
## 118 NA NA 0.078 0.078
## 119 NA NA 18.655 18.655
## 120 NA NA 5.469 5.469
## 121 NA NA 0.000 0.000
## 122 NA NA 0.000 0.000
## 123 NA NA 0.000 0.000
## 124 NA NA 0.000 0.000
## 125 -1.116 0.265 -0.327 0.090
## 126 -0.457 0.648 -0.066 0.041
## 127 0.905 0.366 -0.031 0.085
## 128 -3.092 0.002 -0.367 -0.082
## 129 -0.087 0.931 -0.042 0.038
## 130 -1.859 0.063 -0.367 0.010
## 131 -0.426 0.670 -0.130 0.084
## 132 -0.426 0.670 -0.157 0.101
## 133 1.125 0.260 -0.092 0.339
## 134 1.800 0.072 -0.001 0.028Emotional Cost
cost_em <-'
#Measurement
hs_prep_1 =~ pre_hs_prep_1 + pre_hs_prep_2 + pre_hs_prep_3 + pre_hs_prep_4 + pre_hs_prep_5
expect =~ pre_exp_1 + pre_exp_2 + pre_exp_3
value =~ pre_val_1 + pre_val_2 + pre_val_3
a_em_cost =~ pre_cost_em_1 + pre_cost_em_2 + pre_cost_em_3 + pre_cost_em_4 + pre_cost_em_5 + pre_cost_em_6
#Regressions
a_em_cost ~ aa*hs_prep_1 + ab*pre_hours_work + ac*pre_hours_math_prep + ad*credits_more_than_15 + ae*pre_stem_int + af*expect + ag*value + ah*female + ai*urm + aj*Best_MPS
eoc_cost_em ~ b*a_em_cost + hs_prep_1 + pre_hours_work + pre_hours_math_prep + credits_more_than_15 + pre_stem_int + expect + value + female + urm + Best_MPS + week
#indirect effects
hs_cost:= aa*b
work_cost:= ab*b
prep_cost:= ac*b
credits_cost:= ad*b
stem_cost:= ae*b
expect_cost:= af*b
val_cost:= ag*b
female_cost:= ah*b
urm_cost:= ai*b
mps_cost:= aj*b
#Covariances
# pre_stem_int ~~ expect + value + a_te_cost
# expect ~~ value + a_te_cost
# value ~~ a_te_cost
# pre_hours_math_prep ~~ pre_hours_math_prep
'
fit <- sem(cost_em, data=MTH_132_124_all, estimator = "MLR", cluster = "stud_id", missing = "ML.x", se = 'bootstrap')
test = parameterEstimates(fit,boot.ci.type = 'bca.simple',level=.95) # bootstrapped estimate
summary(fit, fit.measures = TRUE, standardized = TRUE)## lavaan 0.6-7 ended normally after 95 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of free parameters 78
##
## Number of observations 2435
## Number of clusters [stud_id] 429
## Number of missing patterns 43
##
## Model Test User Model:
## Standard Robust
## Test Statistic 2438.350 340.952
## Degrees of freedom 255 255
## P-value (Chi-square) 0.000 0.000
## Scaling correction factor 7.152
## Yuan-Bentler correction (Mplus variant)
##
## Model Test Baseline Model:
##
## Test statistic 21940.210 2756.242
## Degrees of freedom 297 297
## P-value 0.000 0.000
## Scaling correction factor 7.960
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.899 0.965
## Tucker-Lewis Index (TLI) 0.883 0.959
##
## Robust Comparative Fit Index (CFI) 0.969
## Robust Tucker-Lewis Index (TLI) 0.963
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) NA NA
## Scaling correction factor 11.212
## for the MLR correction
## Loglikelihood unrestricted model (H1) NA NA
## Scaling correction factor 8.103
## for the MLR correction
##
## Akaike (AIC) NA NA
## Bayesian (BIC) NA NA
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.059 0.012
## 90 Percent confidence interval - lower 0.057 0.011
## 90 Percent confidence interval - upper 0.061 0.013
## P-value RMSEA <= 0.05 0.000 1.000
##
## Robust RMSEA 0.031
## 90 Percent confidence interval - lower 0.022
## 90 Percent confidence interval - upper 0.040
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.064 0.064
##
## Parameter Estimates:
##
## Standard errors Robust.cluster
## Information Observed
## Observed information based on Hessian
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## hs_prep_1 =~
## pre_hs_prep_1 1.000 0.693 0.526
## pre_hs_prep_2 0.735 0.108 6.836 0.000 0.509 0.622
## pre_hs_prep_3 0.751 0.117 6.400 0.000 0.520 0.786
## pre_hs_prep_4 0.787 0.125 6.305 0.000 0.546 0.836
## pre_hs_prep_5 0.785 0.149 5.272 0.000 0.544 0.653
## expect =~
## pre_exp_1 1.000 0.881 0.760
## pre_exp_2 0.910 0.076 12.032 0.000 0.801 0.821
## pre_exp_3 1.144 0.084 13.553 0.000 1.008 0.840
## value =~
## pre_val_1 1.000 1.026 0.843
## pre_val_2 0.955 0.052 18.312 0.000 0.979 0.852
## pre_val_3 1.048 0.053 19.729 0.000 1.075 0.846
## a_em_cost =~
## pre_cost_em_1 1.000 1.253 0.775
## pre_cost_em_2 0.906 0.064 14.234 0.000 1.135 0.797
## pre_cost_em_3 0.931 0.072 12.927 0.000 1.167 0.749
## pre_cost_em_4 0.952 0.067 14.236 0.000 1.193 0.804
## pre_cost_em_5 1.007 0.057 17.683 0.000 1.262 0.826
## pre_cost_em_6 1.029 0.062 16.641 0.000 1.290 0.797
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## a_em_cost ~
## hs_prep_1 (aa) -0.358 0.220 -1.625 0.104 -0.198 -0.198
## pr_hrs_wr (ab) -0.007 0.061 -0.119 0.905 -0.006 -0.009
## pr_hrs_m_ (ac) 0.062 0.059 1.062 0.288 0.050 0.066
## crdt___15 (ad) -0.328 0.134 -2.453 0.014 -0.262 -0.126
## pr_stm_nt (ae) 0.001 0.044 0.025 0.980 0.001 0.002
## expect (af) -0.642 0.189 -3.398 0.001 -0.452 -0.452
## value (ag) 0.104 0.125 0.832 0.406 0.085 0.085
## female (ah) 0.212 0.144 1.479 0.139 0.169 0.084
## urm (ai) 0.182 0.254 0.716 0.474 0.145 0.039
## Best_MPS (aj) -0.017 0.014 -1.201 0.230 -0.014 -0.070
## eoc_cost_em ~
## a_em_cost (b) 0.534 0.073 7.308 0.000 0.669 0.385
## hs_prep_1 -0.150 0.163 -0.921 0.357 -0.104 -0.060
## pr_hrs_wr 0.011 0.051 0.213 0.831 0.011 0.010
## pr_hrs_m_ 0.081 0.056 1.437 0.151 0.081 0.061
## crdt___15 -0.261 0.150 -1.733 0.083 -0.261 -0.072
## pr_stm_nt -0.044 0.041 -1.065 0.287 -0.044 -0.046
## expect -0.335 0.139 -2.405 0.016 -0.295 -0.170
## value 0.111 0.124 0.896 0.370 0.114 0.066
## female -0.006 0.149 -0.037 0.970 -0.006 -0.002
## urm 0.346 0.274 1.259 0.208 0.346 0.054
## Best_MPS -0.051 0.015 -3.323 0.001 -0.051 -0.147
## week 0.003 0.012 0.285 0.776 0.003 0.006
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## hs_prep_1 ~~
## expect 0.237 0.089 2.659 0.008 0.388 0.388
## value 0.202 0.101 1.988 0.047 0.284 0.284
## expect ~~
## value 0.611 0.131 4.665 0.000 0.676 0.676
##
## Intercepts:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pre_hs_prep_1 4.353 0.109 39.867 0.000 4.353 3.306
## .pre_hs_prep_2 4.930 0.071 69.285 0.000 4.930 6.018
## .pre_hs_prep_3 5.306 0.056 94.639 0.000 5.306 8.014
## .pre_hs_prep_4 5.443 0.058 94.328 0.000 5.443 8.342
## .pre_hs_prep_5 5.131 0.070 73.695 0.000 5.131 6.164
## .pre_exp_1 5.715 0.066 86.172 0.000 5.715 4.932
## .pre_exp_2 5.923 0.057 103.369 0.000 5.923 6.069
## .pre_exp_3 5.496 0.069 79.367 0.000 5.496 4.578
## .pre_val_1 5.805 0.071 81.553 0.000 5.805 4.767
## .pre_val_2 5.659 0.067 84.400 0.000 5.659 4.920
## .pre_val_3 5.575 0.075 74.628 0.000 5.575 4.385
## .pre_cost_em_1 4.206 0.501 8.389 0.000 4.206 2.602
## .pre_cost_em_2 3.582 0.450 7.953 0.000 3.582 2.514
## .pre_cost_em_3 3.936 0.464 8.485 0.000 3.936 2.526
## .pre_cost_em_4 3.644 0.475 7.677 0.000 3.644 2.454
## .pre_cost_em_5 3.863 0.502 7.689 0.000 3.863 2.529
## .pre_cost_em_6 3.891 0.516 7.535 0.000 3.891 2.404
## .eoc_cost_em 4.181 0.510 8.199 0.000 4.181 2.407
## hs_prep_1 0.000 0.000 0.000
## expect 0.000 0.000 0.000
## value 0.000 0.000 0.000
## .a_em_cost 0.000 0.000 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .pre_hs_prep_1 1.254 0.177 7.066 0.000 1.254 0.723
## .pre_hs_prep_2 0.412 0.067 6.153 0.000 0.412 0.613
## .pre_hs_prep_3 0.168 0.033 5.015 0.000 0.168 0.383
## .pre_hs_prep_4 0.128 0.029 4.359 0.000 0.128 0.301
## .pre_hs_prep_5 0.397 0.149 2.658 0.008 0.397 0.574
## .pre_exp_1 0.567 0.159 3.560 0.000 0.567 0.422
## .pre_exp_2 0.310 0.060 5.178 0.000 0.310 0.326
## .pre_exp_3 0.425 0.086 4.923 0.000 0.425 0.295
## .pre_val_1 0.430 0.096 4.478 0.000 0.430 0.290
## .pre_val_2 0.363 0.060 6.087 0.000 0.363 0.275
## .pre_val_3 0.461 0.087 5.320 0.000 0.461 0.285
## .pre_cost_em_1 1.044 0.114 9.168 0.000 1.044 0.399
## .pre_cost_em_2 0.741 0.086 8.669 0.000 0.741 0.365
## .pre_cost_em_3 1.067 0.135 7.885 0.000 1.067 0.440
## .pre_cost_em_4 0.781 0.119 6.584 0.000 0.781 0.354
## .pre_cost_em_5 0.741 0.108 6.836 0.000 0.741 0.318
## .pre_cost_em_6 0.957 0.112 8.541 0.000 0.957 0.365
## .eoc_cost_em 2.120 0.120 17.604 0.000 2.120 0.702
## hs_prep_1 0.480 0.128 3.745 0.000 1.000 1.000
## expect 0.776 0.160 4.865 0.000 1.000 1.000
## value 1.053 0.186 5.672 0.000 1.000 1.000
## .a_em_cost 1.112 0.184 6.047 0.000 0.708 0.708
##
## Defined Parameters:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## hs_cost -0.191 0.118 -1.625 0.104 -0.132 -0.076
## work_cost -0.004 0.033 -0.119 0.905 -0.004 -0.003
## prep_cost 0.033 0.031 1.063 0.288 0.033 0.025
## credits_cost -0.175 0.077 -2.260 0.024 -0.175 -0.048
## stem_cost 0.001 0.023 0.025 0.980 0.001 0.001
## expect_cost -0.343 0.114 -3.013 0.003 -0.302 -0.174
## val_cost 0.055 0.066 0.835 0.404 0.057 0.033
## female_cost 0.113 0.077 1.471 0.141 0.113 0.032
## urm_cost 0.097 0.137 0.710 0.478 0.097 0.015
## mps_cost -0.009 0.008 -1.203 0.229 -0.009 -0.027test## lhs op rhs label est se
## 1 hs_prep_1 =~ pre_hs_prep_1 1.000 0.000
## 2 hs_prep_1 =~ pre_hs_prep_2 0.735 0.108
## 3 hs_prep_1 =~ pre_hs_prep_3 0.751 0.117
## 4 hs_prep_1 =~ pre_hs_prep_4 0.787 0.125
## 5 hs_prep_1 =~ pre_hs_prep_5 0.785 0.149
## 6 expect =~ pre_exp_1 1.000 0.000
## 7 expect =~ pre_exp_2 0.910 0.076
## 8 expect =~ pre_exp_3 1.144 0.084
## 9 value =~ pre_val_1 1.000 0.000
## 10 value =~ pre_val_2 0.955 0.052
## 11 value =~ pre_val_3 1.048 0.053
## 12 a_em_cost =~ pre_cost_em_1 1.000 0.000
## 13 a_em_cost =~ pre_cost_em_2 0.906 0.064
## 14 a_em_cost =~ pre_cost_em_3 0.931 0.072
## 15 a_em_cost =~ pre_cost_em_4 0.952 0.067
## 16 a_em_cost =~ pre_cost_em_5 1.007 0.057
## 17 a_em_cost =~ pre_cost_em_6 1.029 0.062
## 18 a_em_cost ~ hs_prep_1 aa -0.358 0.220
## 19 a_em_cost ~ pre_hours_work ab -0.007 0.061
## 20 a_em_cost ~ pre_hours_math_prep ac 0.062 0.059
## 21 a_em_cost ~ credits_more_than_15 ad -0.328 0.134
## 22 a_em_cost ~ pre_stem_int ae 0.001 0.044
## 23 a_em_cost ~ expect af -0.642 0.189
## 24 a_em_cost ~ value ag 0.104 0.125
## 25 a_em_cost ~ female ah 0.212 0.144
## 26 a_em_cost ~ urm ai 0.182 0.254
## 27 a_em_cost ~ Best_MPS aj -0.017 0.014
## 28 eoc_cost_em ~ a_em_cost b 0.534 0.073
## 29 eoc_cost_em ~ hs_prep_1 -0.150 0.163
## 30 eoc_cost_em ~ pre_hours_work 0.011 0.051
## 31 eoc_cost_em ~ pre_hours_math_prep 0.081 0.056
## 32 eoc_cost_em ~ credits_more_than_15 -0.261 0.150
## 33 eoc_cost_em ~ pre_stem_int -0.044 0.041
## 34 eoc_cost_em ~ expect -0.335 0.139
## 35 eoc_cost_em ~ value 0.111 0.124
## 36 eoc_cost_em ~ female -0.006 0.149
## 37 eoc_cost_em ~ urm 0.346 0.274
## 38 eoc_cost_em ~ Best_MPS -0.051 0.015
## 39 eoc_cost_em ~ week 0.003 0.012
## 40 pre_hs_prep_1 ~~ pre_hs_prep_1 1.254 0.177
## 41 pre_hs_prep_2 ~~ pre_hs_prep_2 0.412 0.067
## 42 pre_hs_prep_3 ~~ pre_hs_prep_3 0.168 0.033
## 43 pre_hs_prep_4 ~~ pre_hs_prep_4 0.128 0.029
## 44 pre_hs_prep_5 ~~ pre_hs_prep_5 0.397 0.149
## 45 pre_exp_1 ~~ pre_exp_1 0.567 0.159
## 46 pre_exp_2 ~~ pre_exp_2 0.310 0.060
## 47 pre_exp_3 ~~ pre_exp_3 0.425 0.086
## 48 pre_val_1 ~~ pre_val_1 0.430 0.096
## 49 pre_val_2 ~~ pre_val_2 0.363 0.060
## 50 pre_val_3 ~~ pre_val_3 0.461 0.087
## 51 pre_cost_em_1 ~~ pre_cost_em_1 1.044 0.114
## 52 pre_cost_em_2 ~~ pre_cost_em_2 0.741 0.086
## 53 pre_cost_em_3 ~~ pre_cost_em_3 1.067 0.135
## 54 pre_cost_em_4 ~~ pre_cost_em_4 0.781 0.119
## 55 pre_cost_em_5 ~~ pre_cost_em_5 0.741 0.108
## 56 pre_cost_em_6 ~~ pre_cost_em_6 0.957 0.112
## 57 eoc_cost_em ~~ eoc_cost_em 2.120 0.120
## 58 hs_prep_1 ~~ hs_prep_1 0.480 0.128
## 59 expect ~~ expect 0.776 0.160
## 60 value ~~ value 1.053 0.186
## 61 a_em_cost ~~ a_em_cost 1.112 0.184
## 62 hs_prep_1 ~~ expect 0.237 0.089
## 63 hs_prep_1 ~~ value 0.202 0.101
## 64 expect ~~ value 0.611 0.131
## 65 pre_hours_work ~~ pre_hours_work 2.327 0.000
## 66 pre_hours_work ~~ pre_hours_math_prep 0.159 0.000
## 67 pre_hours_work ~~ credits_more_than_15 -0.031 0.000
## 68 pre_hours_work ~~ pre_stem_int 0.329 0.000
## 69 pre_hours_work ~~ female 0.089 0.000
## 70 pre_hours_work ~~ urm 0.087 0.000
## 71 pre_hours_work ~~ Best_MPS -0.545 0.000
## 72 pre_hours_work ~~ week -0.012 0.000
## 73 pre_hours_math_prep ~~ pre_hours_math_prep 1.744 0.000
## 74 pre_hours_math_prep ~~ credits_more_than_15 -0.033 0.000
## 75 pre_hours_math_prep ~~ pre_stem_int 0.056 0.000
## 76 pre_hours_math_prep ~~ female -0.053 0.000
## 77 pre_hours_math_prep ~~ urm -0.003 0.000
## 78 pre_hours_math_prep ~~ Best_MPS 0.466 0.000
## 79 pre_hours_math_prep ~~ week -0.082 0.000
## 80 credits_more_than_15 ~~ credits_more_than_15 0.231 0.000
## 81 credits_more_than_15 ~~ pre_stem_int -0.068 0.000
## 82 credits_more_than_15 ~~ female 0.007 0.000
## 83 credits_more_than_15 ~~ urm 0.007 0.000
## 84 credits_more_than_15 ~~ Best_MPS -0.108 0.000
## 85 credits_more_than_15 ~~ week 0.004 0.000
## 86 pre_stem_int ~~ pre_stem_int 3.382 0.000
## 87 pre_stem_int ~~ female 0.006 0.000
## 88 pre_stem_int ~~ urm 0.030 0.000
## 89 pre_stem_int ~~ Best_MPS 0.344 0.000
## 90 pre_stem_int ~~ week -0.126 0.000
## 91 female ~~ female 0.243 0.000
## 92 female ~~ urm 0.012 0.000
## 93 female ~~ Best_MPS -0.217 0.000
## 94 female ~~ week 0.077 0.000
## 95 urm ~~ urm 0.072 0.000
## 96 urm ~~ Best_MPS -0.376 0.000
## 97 urm ~~ week -0.008 0.000
## 98 Best_MPS ~~ Best_MPS 25.700 0.000
## 99 Best_MPS ~~ week -0.381 0.000
## 100 week ~~ week 9.922 0.000
## 101 pre_hs_prep_1 ~1 4.353 0.109
## 102 pre_hs_prep_2 ~1 4.930 0.071
## 103 pre_hs_prep_3 ~1 5.306 0.056
## 104 pre_hs_prep_4 ~1 5.443 0.058
## 105 pre_hs_prep_5 ~1 5.131 0.070
## 106 pre_exp_1 ~1 5.715 0.066
## 107 pre_exp_2 ~1 5.923 0.057
## 108 pre_exp_3 ~1 5.496 0.069
## 109 pre_val_1 ~1 5.805 0.071
## 110 pre_val_2 ~1 5.659 0.067
## 111 pre_val_3 ~1 5.575 0.075
## 112 pre_cost_em_1 ~1 4.206 0.501
## 113 pre_cost_em_2 ~1 3.582 0.450
## 114 pre_cost_em_3 ~1 3.936 0.464
## 115 pre_cost_em_4 ~1 3.644 0.475
## 116 pre_cost_em_5 ~1 3.863 0.502
## 117 pre_cost_em_6 ~1 3.891 0.516
## 118 eoc_cost_em ~1 4.181 0.510
## 119 pre_hours_work ~1 2.189 0.000
## 120 pre_hours_math_prep ~1 3.310 0.000
## 121 credits_more_than_15 ~1 0.363 0.000
## 122 pre_stem_int ~1 5.043 0.000
## 123 female ~1 0.417 0.000
## 124 urm ~1 0.078 0.000
## 125 Best_MPS ~1 18.680 0.000
## 126 week ~1 5.469 0.000
## 127 hs_prep_1 ~1 0.000 0.000
## 128 expect ~1 0.000 0.000
## 129 value ~1 0.000 0.000
## 130 a_em_cost ~1 0.000 0.000
## 131 hs_cost := aa*b hs_cost -0.191 0.118
## 132 work_cost := ab*b work_cost -0.004 0.033
## 133 prep_cost := ac*b prep_cost 0.033 0.031
## 134 credits_cost := ad*b credits_cost -0.175 0.077
## 135 stem_cost := ae*b stem_cost 0.001 0.023
## 136 expect_cost := af*b expect_cost -0.343 0.114
## 137 val_cost := ag*b val_cost 0.055 0.066
## 138 female_cost := ah*b female_cost 0.113 0.077
## 139 urm_cost := ai*b urm_cost 0.097 0.137
## 140 mps_cost := aj*b mps_cost -0.009 0.008
## z pvalue ci.lower ci.upper
## 1 NA NA 1.000 1.000
## 2 6.836 0.000 0.524 0.946
## 3 6.400 0.000 0.521 0.980
## 4 6.305 0.000 0.543 1.032
## 5 5.272 0.000 0.493 1.076
## 6 NA NA 1.000 1.000
## 7 12.032 0.000 0.761 1.058
## 8 13.553 0.000 0.979 1.310
## 9 NA NA 1.000 1.000
## 10 18.312 0.000 0.852 1.057
## 11 19.729 0.000 0.944 1.152
## 12 NA NA 1.000 1.000
## 13 14.234 0.000 0.781 1.031
## 14 12.927 0.000 0.790 1.072
## 15 14.236 0.000 0.821 1.083
## 16 17.683 0.000 0.895 1.118
## 17 16.641 0.000 0.908 1.150
## 18 -1.625 0.104 -0.790 0.074
## 19 -0.119 0.905 -0.127 0.113
## 20 1.062 0.288 -0.053 0.178
## 21 -2.453 0.014 -0.590 -0.066
## 22 0.025 0.980 -0.085 0.087
## 23 -3.398 0.001 -1.013 -0.272
## 24 0.832 0.406 -0.141 0.349
## 25 1.479 0.139 -0.069 0.494
## 26 0.716 0.474 -0.315 0.678
## 27 -1.201 0.230 -0.046 0.011
## 28 7.308 0.000 0.391 0.677
## 29 -0.921 0.357 -0.470 0.169
## 30 0.213 0.831 -0.089 0.111
## 31 1.437 0.151 -0.029 0.191
## 32 -1.733 0.083 -0.555 0.034
## 33 -1.065 0.287 -0.124 0.037
## 34 -2.405 0.016 -0.607 -0.062
## 35 0.896 0.370 -0.132 0.354
## 36 -0.037 0.970 -0.298 0.287
## 37 1.259 0.208 -0.192 0.884
## 38 -3.323 0.001 -0.080 -0.021
## 39 0.285 0.776 -0.019 0.026
## 40 7.066 0.000 0.906 1.602
## 41 6.153 0.000 0.280 0.543
## 42 5.015 0.000 0.102 0.233
## 43 4.359 0.000 0.070 0.186
## 44 2.658 0.008 0.104 0.690
## 45 3.560 0.000 0.255 0.879
## 46 5.178 0.000 0.193 0.427
## 47 4.923 0.000 0.256 0.594
## 48 4.478 0.000 0.242 0.618
## 49 6.087 0.000 0.246 0.480
## 50 5.320 0.000 0.291 0.631
## 51 9.168 0.000 0.821 1.267
## 52 8.669 0.000 0.574 0.909
## 53 7.885 0.000 0.802 1.333
## 54 6.584 0.000 0.548 1.013
## 55 6.836 0.000 0.529 0.954
## 56 8.541 0.000 0.738 1.177
## 57 17.604 0.000 1.884 2.356
## 58 3.745 0.000 0.229 0.731
## 59 4.865 0.000 0.464 1.089
## 60 5.672 0.000 0.689 1.417
## 61 6.047 0.000 0.752 1.473
## 62 2.659 0.008 0.062 0.411
## 63 1.988 0.047 0.003 0.401
## 64 4.665 0.000 0.355 0.868
## 65 NA NA 2.327 2.327
## 66 NA NA 0.159 0.159
## 67 NA NA -0.031 -0.031
## 68 NA NA 0.329 0.329
## 69 NA NA 0.089 0.089
## 70 NA NA 0.087 0.087
## 71 NA NA -0.545 -0.545
## 72 NA NA -0.012 -0.012
## 73 NA NA 1.744 1.744
## 74 NA NA -0.033 -0.033
## 75 NA NA 0.056 0.056
## 76 NA NA -0.053 -0.053
## 77 NA NA -0.003 -0.003
## 78 NA NA 0.466 0.466
## 79 NA NA -0.082 -0.082
## 80 NA NA 0.231 0.231
## 81 NA NA -0.068 -0.068
## 82 NA NA 0.007 0.007
## 83 NA NA 0.007 0.007
## 84 NA NA -0.108 -0.108
## 85 NA NA 0.004 0.004
## 86 NA NA 3.382 3.382
## 87 NA NA 0.006 0.006
## 88 NA NA 0.030 0.030
## 89 NA NA 0.344 0.344
## 90 NA NA -0.126 -0.126
## 91 NA NA 0.243 0.243
## 92 NA NA 0.012 0.012
## 93 NA NA -0.217 -0.217
## 94 NA NA 0.077 0.077
## 95 NA NA 0.072 0.072
## 96 NA NA -0.376 -0.376
## 97 NA NA -0.008 -0.008
## 98 NA NA 25.700 25.700
## 99 NA NA -0.381 -0.381
## 100 NA NA 9.922 9.922
## 101 39.867 0.000 4.139 4.567
## 102 69.285 0.000 4.791 5.070
## 103 94.639 0.000 5.196 5.415
## 104 94.328 0.000 5.330 5.556
## 105 73.695 0.000 4.995 5.267
## 106 86.172 0.000 5.585 5.845
## 107 103.369 0.000 5.811 6.035
## 108 79.367 0.000 5.360 5.632
## 109 81.553 0.000 5.665 5.944
## 110 84.400 0.000 5.527 5.790
## 111 74.628 0.000 5.429 5.722
## 112 8.389 0.000 3.224 5.189
## 113 7.953 0.000 2.699 4.464
## 114 8.485 0.000 3.027 4.845
## 115 7.677 0.000 2.713 4.574
## 116 7.689 0.000 2.879 4.848
## 117 7.535 0.000 2.879 4.903
## 118 8.199 0.000 3.182 5.181
## 119 NA NA 2.189 2.189
## 120 NA NA 3.310 3.310
## 121 NA NA 0.363 0.363
## 122 NA NA 5.043 5.043
## 123 NA NA 0.417 0.417
## 124 NA NA 0.078 0.078
## 125 NA NA 18.680 18.680
## 126 NA NA 5.469 5.469
## 127 NA NA 0.000 0.000
## 128 NA NA 0.000 0.000
## 129 NA NA 0.000 0.000
## 130 NA NA 0.000 0.000
## 131 -1.625 0.104 -0.422 0.039
## 132 -0.119 0.905 -0.068 0.060
## 133 1.063 0.288 -0.028 0.095
## 134 -2.260 0.024 -0.327 -0.023
## 135 0.025 0.980 -0.045 0.046
## 136 -3.013 0.003 -0.566 -0.120
## 137 0.835 0.404 -0.075 0.186
## 138 1.471 0.141 -0.038 0.264
## 139 0.710 0.478 -0.171 0.365
## 140 -1.203 0.229 -0.024 0.006Multilevel SEM model
# cost_te <-'
# level: 1
# eoc_cost_te ~~ eoc_cost_te
#
# level: 2
# #Measurement
# hs_prep_1 =~ pre_hs_prep_1 + pre_hs_prep_2 + pre_hs_prep_3 + pre_hs_prep_4 + pre_hs_prep_5
# expect =~ pre_exp_1 + pre_exp_2 + pre_exp_3
# value =~ pre_val_1 + pre_val_2 + pre_val_3
# a_te_cost =~ pre_cost_te_1 + pre_cost_te_2 + pre_cost_te_3 + pre_cost_te_4 + pre_cost_te_5
# b_te_cost =~ post_cost_te_1 + post_cost_te_2 + post_cost_te_3 + post_cost_te_4 + post_cost_te_5
#
# #Regressions
# b_te_cost ~ hs_prep_1 + pre_hours_work + Msu_Lt_Atmpt_Hours + pre_stem_int + expect + value + a_te_cost + eoc_cost_te
# eoc_cost_te ~ hs_prep_1 + pre_hours_work + Msu_Lt_Atmpt_Hours + pre_stem_int + expect + value + a_te_cost
#
# #Covariances
# pre_stem_int ~~ expect + value + a_te_cost
# expect ~~ value + a_te_cost
# value ~~ a_te_cost
# pre_hours_class_prep ~~ pre_hours_math_prep
# '
#
# fit <- sem(cost_te, data=MTH_132_124_all, cluster = "stud_id", missing = "ML.x")
# summary(fit, fit.measures = TRUE, standardized = TRUE)# cost_oe <-'
# level: 1
# eoc_cost_oe ~~ eoc_cost_oe
#
# level: 2
# #Measurement
# hs_prep_1 =~ pre_hs_prep_1 + pre_hs_prep_2 + pre_hs_prep_3 + pre_hs_prep_4 + pre_hs_prep_5
# expect =~ pre_exp_1 + pre_exp_2 + pre_exp_3
# value =~ pre_val_1 + pre_val_2 + pre_val_3
# a_oe_cost =~ pre_cost_oe_1 + pre_cost_oe_2 + pre_cost_oe_3 + pre_cost_oe_4
# b_oe_cost =~ post_cost_oe_1 + post_cost_oe_2 + post_cost_oe_3 + post_cost_oe_4
#
# #Regressions
# b_oe_cost ~ hs_prep_1 + pre_hours_work + Msu_Lt_Atmpt_Hours + pre_stem_int + expect + value + a_oe_cost + eoc_cost_oe
# eoc_cost_oe ~ hs_prep_1 + pre_hours_work + Msu_Lt_Atmpt_Hours + pre_stem_int + expect + value + a_oe_cost
#
# #Covariances
# pre_stem_int ~~ expect + value + a_oe_cost
# expect ~~ value + a_oe_cost
# value ~~ a_oe_cost
# pre_hours_class_prep ~~ pre_hours_math_prep
# '
#
# fit <- sem(cost_oe, data=MTH_132_124_all, cluster = "stud_id", missing = "ML.x")
# summary(fit, fit.measures = TRUE, standardized = TRUE)# cost_lv <-'
# level: 1
# eoc_cost_lv ~~ eoc_cost_lv
#
# level: 2
# #Measurement
# hs_prep_1 =~ pre_hs_prep_1 + pre_hs_prep_2 + pre_hs_prep_3 + pre_hs_prep_4 + pre_hs_prep_5
# expect =~ pre_exp_1 + pre_exp_2 + pre_exp_3
# value =~ pre_val_1 + pre_val_2 + pre_val_3
# a_lv_cost =~ pre_cost_lv_1 + pre_cost_lv_2 + pre_cost_lv_3 + pre_cost_lv_4
# b_lv_cost =~ post_cost_lv_1 + post_cost_lv_2 + post_cost_lv_3 + post_cost_lv_4
#
# #Regressions
# b_lv_cost ~ hs_prep_1 + pre_hours_work + Msu_Lt_Atmpt_Hours + pre_stem_int + expect + value + a_lv_cost + eoc_cost_lv
# eoc_cost_lv ~ hs_prep_1 + pre_hours_work + Msu_Lt_Atmpt_Hours + pre_stem_int + expect + value + a_lv_cost
#
# #Covariances
# pre_stem_int ~~ expect + value + a_lv_cost
# expect ~~ value + a_lv_cost
# value ~~ a_lv_cost
# pre_hours_class_prep ~~ pre_hours_math_prep
# '
#
# fit <- sem(cost_lv, data=MTH_132_124_all, cluster = "stud_id", missing = "ML.x")
# summary(fit, fit.measures = TRUE, standardized = TRUE)# cost_em <-'
# level: 1
# eoc_cost_em ~~ eoc_cost_em
#
# level: 2
# #Measurement
# hs_prep_1 =~ pre_hs_prep_1 + pre_hs_prep_2 + pre_hs_prep_3 + pre_hs_prep_4 + pre_hs_prep_5
# expect =~ pre_exp_1 + pre_exp_2 + pre_exp_3
# value =~ pre_val_1 + pre_val_2 + pre_val_3
# a_em_cost =~ pre_cost_em_1 + pre_cost_em_2 + pre_cost_em_3 + pre_cost_em_4 + pre_cost_em_5 + pre_cost_em_6
# b_em_cost =~ post_cost_em_1 + post_cost_em_2 + post_cost_em_3 + post_cost_em_4 + post_cost_em_5 + post_cost_em_6
#
# #Regressions
# b_em_cost ~ hs_prep_1 + pre_hours_work + Msu_Lt_Atmpt_Hours + pre_stem_int + expect + value + a_em_cost + eoc_cost_em
# eoc_cost_em ~ hs_prep_1 + pre_hours_work + Msu_Lt_Atmpt_Hours + pre_stem_int + expect + value + a_em_cost
#
# #Covariances
# pre_stem_int ~~ expect + value + a_em_cost
# expect ~~ value + a_em_cost
# value ~~ a_em_cost
# pre_hours_class_prep ~~ pre_hours_math_prep
# '
#
# fit <- sem(cost_em, data=MTH_132_124_all, cluster = "stud_id", missing = "ML.x")
# summary(fit, fit.measures = TRUE, standardized = TRUE)MTH_132_124_all$engagement <- composite_mean_maker(MTH_132_124_all, eoc_conc, eoc_hard_work)
M0 <- lmer(engagement ~ eoc_con + eoc_val + eoc_confused + eoc_confused*eoc_val +
female + week +
(1|stud_id),
data = MTH_132_124_all, control=lmerControl(optimizer="bobyqa"))
summary(M0)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: engagement ~ eoc_con + eoc_val + eoc_confused + eoc_confused *
## eoc_val + female + week + (1 | stud_id)
## Data: MTH_132_124_all
## Control: lmerControl(optimizer = "bobyqa")
##
## REML criterion at convergence: 6282.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.6462 -0.5253 0.0597 0.5963 4.1065
##
## Random effects:
## Groups Name Variance Std.Dev.
## stud_id (Intercept) 0.4503 0.6710
## Residual 0.7795 0.8829
## Number of obs: 2210, groups: stud_id, 415
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.978e+00 1.602e-01 2.004e+03 12.346 < 2e-16 ***
## eoc_con 1.329e-01 1.664e-02 2.202e+03 7.991 2.14e-15 ***
## eoc_val 4.204e-01 2.678e-02 2.168e+03 15.697 < 2e-16 ***
## eoc_confused 2.117e-01 3.664e-02 2.202e+03 5.778 8.63e-09 ***
## female 1.491e-01 8.228e-02 3.609e+02 1.812 0.07083 .
## week -1.717e-02 6.411e-03 2.030e+03 -2.678 0.00747 **
## eoc_val:eoc_confused -3.054e-02 7.434e-03 2.201e+03 -4.107 4.15e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) eoc_cn eoc_vl ec_cnf female week
## eoc_con -0.435
## eoc_val -0.693 -0.172
## eoc_confusd -0.730 0.158 0.702
## female -0.215 0.017 0.017 0.008
## week -0.283 0.034 0.096 0.046 -0.035
## ec_vl:c_cnf 0.618 -0.014 -0.794 -0.905 -0.015 -0.044performance::icc(M0, by_group = T)## # ICC by Group
##
## Group | ICC
## ---------------
## stud_id | 0.366library(sjPlot)## Warning: package 'sjPlot' was built under R version 4.0.2library(sjmisc)##
## Attaching package: 'sjmisc'## The following object is masked from 'package:Hmisc':
##
## %nin%## The following object is masked from 'package:purrr':
##
## is_empty## The following object is masked from 'package:tidyr':
##
## replace_na## The following object is masked from 'package:tibble':
##
## add_caselibrary(ggplot2)
# library(lavaan)
# calc_emo <- '
# #measurement model
# control =~ eoc_con + eoc_comp
# value =~ eoc_val + eoc_future_goals
# engage =~ eoc_conc + eoc_hard_work
#
# #regressions
# #direct effects
# engage ~ eoc_confused + control + value + engage + eoc_confused*value
# eoc_confused ~ control + value
#
# #residual correlations
# control ~~ value
# '
#
# fit <- sem(calc_emo, data=MTH_132_124_all, estimator = "MLR", cluster = "stud_id", missing = "ML.x")
# summary(fit, fit.measures = TRUE, standardized = TRUE)
# standardizedsolution(fit)
#
# MTH_132_124_all$stud